How accelerating technology, weakening competence formation, and the retirement

When Technology Outruns Competence

What if the real bottleneck of the AI age is not technology — but the system required to understand, deploy, verify, and maintain it?

We are used to discussing artificial intelligence primarily as a threat to jobs.
But another problem may become at least as important:

technology may develop faster than the human and organizational capabilities required to understand, implement, verify, and maintain it.

If that happens, the main constraint on economic growth will not necessarily be computing power, software, or access to AI.
It may increasingly become complementary human and organizational capital.
The central risk is not that technology becomes useless.
It is that technological capability grows faster than society’s ability to convert that capability into reliable economic output.

The bottleneck may be shifting
AI is becoming cheaper, more capable, and more widely available.
According to the Stanford AI Index 2025, the share of surveyed organizations reporting AI use increased from 55% in 2023 to 78% in 2024. The share using generative AI in at least one business function rose from 33% to 71%.
Global corporate AI investment reached $252.3 billion, while private AI investment in the United States alone amounted to $109.1 billion.
An enormous accumulation of technological capital is taking place.
Technology itself is not disappearing as a constraint. AI systems still face problems involving reliability, hallucinations, energy consumption, data quality, cybersecurity, semiconductor capacity, and integration.
But in many sectors, the bottleneck may gradually shift from:

access to technology
toward:
the ability to deploy technology effectively.

Technology by itself does not solve a problem.
AI can write a program, perform a calculation, produce a technical diagram, analyze data, identify possible faults, or propose an engineering solution.
Someone must still determine:

AI dramatically reduces the cost of obtaining an answer.
It does not automatically eliminate the need to understand that answer.
This suggests an important relationship:

a competent specialist + AI can produce enormous productivity gains;
an incompetent specialist + AI can produce errors at enormous speed.

The relationship is not literally multiplicative in every task, but in many complex systems AI and human competence behave as complementary inputs.
A useful approximation is:

effective AI value ≈ technological capability × human competence × organizational capability

A weakness in any one of these factors can limit the result.

Technology requires complementary capital
This problem is not unique to AI.
Economists studying general-purpose technologies have long observed that major technologies often require extensive complementary investments before their productivity potential becomes visible.
New technology may require:

new methods of coordination.

Brynjolfsson, Rock, and Syverson describe this phenomenon through the idea of a Productivity J-Curve: firms may make large investments in a transformative technology while measured productivity initially improves only slowly because complementary intangible capital still has to be created.
AI may intensify this problem because the technological component is advancing extraordinarily quickly.
A company can purchase AI systems, computing capacity, robots, industrial software, digital twins, or automated engineering tools.
But between technological capability and actual productivity stand:

An economy can therefore possess extremely advanced technology while lacking the complementary capabilities required to extract its full value.
The problem is more accurately described not as:

excess technology + lack of competence
but as:
rapid growth of technological capital + slower growth of complementary human and organizational capital.

Explicit knowledge becomes cheap; tacit competence may become expensive
The economic value of professional knowledge may therefore change.
AI sharply reduces the cost of retrieving and generating explicit knowledge.
It can explain a formula.
It can generate PLC code.
It can summarize technical documentation.
It can produce a circuit diagram.
It can explain how a variable-frequency drive works.
But professional competence consists of more than explicit knowledge.
It also contains:

knowledge + experience + judgment + context + responsibility

A person may understand how a variable-frequency drive works and still be unable to commission a real industrial installation.
A person may know PLC syntax and still fail to understand the physical process controlled by the PLC.
A person may generate formally correct code while completely misunderstanding the machine on which that code will run.
Much of professional expertise is tacit.
It is accumulated through:

working under uncertainty.

As explicit knowledge becomes cheaper, tacit competence may become relatively more valuable.

The scarce skill may increasingly be not the ability to retrieve an answer, but the ability to determine whether the answer makes sense.

The important shortage may not be “AI programmers”
There will certainly be demand for specialists who build AI systems.
But a more consequential bottleneck may emerge among people working at the interface between digital intelligence and physical, regulated, or safety-critical systems.
These may include:

specialists who simultaneously understand a physical domain, software, and AI.

The causes of shortages differ between professions.
A shortage of electricians does not arise for the same reason as a shortage of cybersecurity specialists.
Labor-market conditions also vary by country and industry.
The common feature is different:

AI output must eventually interact with the real world.

For example, AI can generate a PLC program.
But someone must understand the entire chain:

sensor
↓
electrical diagram
↓
PLC
↓
program
↓
variable-frequency drive
↓
motor
↓
mechanics
↓
technological process
↓
safety

A formally correct piece of generated code may solve very little if nobody understands the complete system.
A machine may fail to start because of:

an incorrect process assumption.

The code may be perfect.
The system may still not work.
The same principle applies in power systems, medicine, chemical plants, transport infrastructure, construction, manufacturing, and other domains in which errors have physical consequences.

Foundational skills were under pressure before generative AI
Educational data do not show a universal decline across every country, subject, and population group.
They do, however, show significant deterioration in several foundational competencies that began before generative AI became widely available.
According to OECD PISA 2022 — Germany, Germany’s results were the lowest recorded for the country in mathematics, reading, and science.
Compared with 2012, the share of German 15-year-olds performing below the basic proficiency level increased by:

11 percentage points in science.

The IQB Bildungstrend 2022 / Kultusministerkonferenz shows a similar pattern.
Between 2015 and 2022, average scores among German ninth-graders fell by:

31 points in spelling.

English performance improved.
This indicates an uneven deterioration in specific competencies rather than evidence of a general decline in intelligence.
The United States shows another version of the same problem.
According to the NAEP Long-Term Trend Assessment 2023, average results among 13-year-olds declined over the previous decade by 7 points in reading and 14 points in mathematics.
More recent NAEP assessments also indicate that the broader weakness has not simply disappeared: 2024 grade-12 reading results and 2024 grade-12 mathematics results remained below their respective 2019 levels.
These assessments do not establish a single cause.
They do not prove that future engineers, doctors, or technicians will necessarily become less competent.
Professional expertise is created not only in schools but also through universities, vocational education, apprenticeships, mentoring, and years of practical work.
The evidence supports a narrower conclusion:

some foundational competencies were already under pressure before generative AI entered schools and workplaces.

That matters because AI is now entering an educational environment in which the formation of basic skills is already challenging.

AI may amplify the problem — or help solve it
Generative AI did not create these educational declines.
But it may affect what happens next.
To become an expert, a person traditionally had to perform a large amount of cognitive work:

make decisions independently.

These activities were not merely a way of producing work.
They were also a way of producing competence.
Professional intuition emerged from repeated exposure to problems.
If AI substitutes for this practice rather than augmenting it, a destructive feedback loop may emerge:

AI performs the difficult task
↓
the person practices less
↓
the person develops less independent understanding
↓
verification of AI becomes more difficult
↓
dependence on AI increases
↓
independent practice declines further

This is one possible mechanism of:

skill degradation through the automation of cognitive practice.

But the opposite outcome is equally possible.
AI can:

help students explore systems faster.

Then the cycle becomes:

AI accelerates feedback
↓
the person experiments more
↓
learning becomes faster
↓
competence grows
↓
AI becomes an increasingly effective professional tool

The critical distinction is therefore not whether AI is used.
It is how AI is used.
AI may accelerate skill formation when it supports deliberate practice.
It may cause skill atrophy when it replaces deliberate practice.
For broader evidence on the relationship between AI adoption and skills, see the OECD - AI and Skills: What We Know So Far.

The paradox of expert formation
This may be the most important long-term issue.
AI can make existing experts dramatically more productive while simultaneously weakening some of the mechanisms through which new experts are created.
An experienced engineer who already understands a process can use AI to:

explore alternative solutions.

These tools may multiply the productivity of someone who already possesses strong domain knowledge.
But junior engineers traditionally develop that domain knowledge by doing many of those same tasks themselves.
They calculate.
They search manuals.
They make design mistakes.
They write poor code.
They debug it.
They misdiagnose faults.
They correct their reasoning.
They slowly build mental models of real systems.
If these developmental tasks are increasingly delegated to AI, an important question appears:

where will the experienced engineer come from ten years later who is capable of correcting the AI itself?

Professional competence is not merely a flow of newly trained workers.
It is a stock accumulated over time.
A simplified representation might be:

H(t+1) = H(t) + training + experience − retirement − obsolescence − deskilling

where H represents the stock of human competence.
AI may simultaneously:

increase the risk of deskilling.

The central question is therefore not simply:

Does AI increase productivity today?

It is also:

Does the system continue to reproduce the human competence required to operate tomorrow?

A society could theoretically consume the professional competence accumulated by previous generations faster than it creates a new generation of experts.
That is a much more serious problem than a temporary shortage of workers.

Skills shortages are already constraining transformation
There is already evidence that companies consider skills shortages an important obstacle to technological change.
According to the World Economic Forum — Future of Jobs Report 2025:

77% of employers plan to reskill or upskill employees in response to AI.

OECD research also suggests that firms with stronger digital skills, ICT infrastructure, and complementary capabilities are more likely to adopt and benefit from AI.
See:

OECD — A Portrait of AI Adopters Across Countries
OECD — Fostering an Inclusive Digital Transformation as AI Spreads Among Firms
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These findings do not prove that a future competence crisis is inevitable.
They establish something narrower:

the ability to absorb and deploy technology already differs significantly between organizations, and skills are already one important constraint.

The open question is whether technological capability will continue to grow faster than deployment capability.

A more realistic model of AI productivity
A simplified production function might be written as:

Y = A × F(K, L, H)

where:

But the productivity effect of AI does not depend only on the existence of AI itself.
It may be more useful to treat productivity as a function of:

A = A(T, H, O)

where:

O — organizational capability.

AI sharply increases T.
Investment in data centers, machinery, energy systems, semiconductor capacity, robotics, and computing infrastructure increases technological and physical capital.
But if H and O grow more slowly, the economy encounters a complementary-capital bottleneck.
The technology remains valuable.
The problem is that additional technological investment may produce less realized output until the complementary system catches up.
This is especially important because human and organizational capital often take much longer to build than computing infrastructure.
A data center can be constructed in years.
A highly competent engineer may require a decade of education and professional experience.
An effective industrial organization may require years of accumulated routines, documentation, culture, and institutional knowledge.
The adjustment speeds are different.

High AI investment may therefore produce temporarily weak realized returns
Suppose a company spends $10 million on AI infrastructure.
Management expects a major productivity increase.
But the company may lack:

internal accountability structures.

The theoretical capability of the system may therefore be much larger than its realized economic value.
This does not necessarily mean that the technology is overvalued or ineffective.
It may mean that complementary investment is incomplete.
This distinction matters.
Weak measured returns during the early stages of adoption may represent:

or simply a delay between investment and measurable output.

The Stanford AI Index 2025 reports that AI adoption is already widespread, but the financial effects reported within individual business functions remain relatively modest in many firms.
Among companies reporting cost savings, most estimate them at less than 10%.
The most commonly reported revenue increase is below 5%.
These figures are not evidence that AI will ultimately generate small productivity gains.
They illustrate that technological capability and realized business value are not the same thing.

The United States has an important advantage
If human capital becomes a major bottleneck, the United States possesses one structural advantage: its historical ability to attract highly qualified specialists from abroad.
The American response can partly be:

insufficient domestic human capital → import human capital

Researchers, engineers, doctors, software specialists, and entrepreneurs have long moved to the United States from:

That may make a competence shortage less damaging for the United States than for economies with less attractive or more closed labor markets.
But professional competence is not equally mobile.
A software researcher may relocate comparatively easily.
A commissioning engineer, electrician, doctor, infrastructure specialist, or industrial technician may face:

The more a profession is embedded in physical infrastructure and local regulation, the harder it is to solve shortages simply through migration.

Economic concentration could increase — but AI also pushes in the opposite direction
If complementary competence becomes scarce, large firms may gain an advantage.
Large technology and industrial corporations can recruit highly qualified specialists globally.
Small firms often cannot.
This may create a reinforcing loop:

the strongest specialists
↓
the largest companies
↓
better AI deployment
↓
higher productivity
↓
more capital
↓
greater ability to recruit scarce specialists

This could widen productivity differences between large corporations and smaller companies.
But AI also creates an opposing force.
Small firms now gain inexpensive access to capabilities that once required large internal departments:

research support.

AI therefore creates two opposing tendencies:

democratization of cognitive capability
and
concentration of complementary capital.

The long-term economic outcome may depend on which effect dominates.

A useful — but limited — analogy with the late 1990s
There is a useful analogy with the internet boom, but it should not be overstated.
In the late 1990s, investors correctly recognized the internet as a transformative technology.
What many misjudged was not the importance of the technology itself, but the speed at which technological potential would become sustainable profit.
Capital flowed into internet companies and telecommunications infrastructure.
Valuations rose rapidly.
The underlying technological transformation was real.
But many investment assumptions proved too optimistic.
In his 2001 speech “What Happened to the New Economy?”, Federal Reserve Governor Laurence H. Meyer examined the productivity acceleration, investment boom, equity-price surge and subsequent retrenchment associated with the technology cycle.
The strongest analogy with AI is therefore not:

AI today = dot-com companies in 2000.

It is:

markets can correctly identify a revolutionary technology while incorrectly estimating the speed of diffusion and the timing of economic returns.

The differences are substantial.
AI software can diffuse extremely quickly.
Many AI investments support assets with real and potentially durable economic value:

robotics.

But diffusion may remain much slower in physical industries such as:

infrastructure.

These sectors require interaction with machines, regulation, safety systems, existing equipment, and skilled professionals.
A potentially unstable combination therefore exists when:

educational and training systems adapt more gradually.

If markets price companies according to expected future AI productivity, while the productivity effect takes much longer to materialize, valuations can temporarily outrun realized economic returns.
The revolutionary technology can still be real.
The timing assumptions can still be wrong.

Why the competence-bottleneck scenario may not occur
There are strong counterarguments.
AI may dramatically accelerate professional education.
Expert systems may reduce the amount of information every worker must personally remember.
Robotics may compensate for some labor shortages.
Higher wages may attract workers into technically demanding professions.
Education systems may adapt.
Companies may redesign apprenticeships and junior professional roles around AI.
International migration may partly compensate for demographic shortages.
AI systems may become much more reliable at verifying other AI systems.
Formal verification, simulation, automated testing, digital twins, standardized interfaces, and safety systems may reduce the amount of human intervention required.
The task itself may also change.
Perhaps future engineers will not need to know everything previous generations knew.
The important skill may become the ability to supervise networks of automated tools rather than perform every intermediate task manually.
In this scenario, AI would not consume human competence.
It would amplify and reproduce it more efficiently.
The outcome is therefore not predetermined.
The critical institutional question is whether education and professional training adapt quickly enough to ensure that AI:

augments the formation of expertise
rather than:
replacing the process through which expertise is formed.

The deeper problem
AI may be creating a new imbalance between different forms of capital.
Technological capability can now increase extremely quickly.
Computing capacity can be purchased.
Software can be copied.
Models can be deployed globally.
But human competence is accumulated more slowly.
A highly capable professional must often pass through:

education
↓
practice
↓
failure
↓
correction
↓
experience
↓
judgment
↓
expertise

Organizational competence develops slowly as well.
Companies accumulate effective procedures, internal standards, documentation, culture, and institutional knowledge over years.
Technology can therefore scale faster than the system required to use it.
This is the central hypothesis:

Quote

The next major economic bottleneck may not be technological capability itself, but the complementary human and organizational capital required to convert technological capability into reliable output.

The greatest long-term risk appears if several processes occur simultaneously:

technological investment continues accelerating.

Then society could begin consuming the stock of professional competence accumulated by previous generations faster than it reproduces it.
That would create a paradox.
Humanity would possess extraordinarily powerful machines.
It would possess enormous computing capacity.
It would possess advanced AI systems capable of producing answers at almost zero marginal cost.
But the scarce resource would become the person capable of asking:

Is this the correct problem?
Does this answer make physical sense?
What happens when the real system behaves differently?
What are the safety consequences?
What has the machine failed to understand?

The most important shortage of the twenty-first century may therefore not be artificial intelligence.
It may be:

Quote

a human being capable of understanding what needs to be done, why it needs to be done, how the real system works, and how to determine when the machine is wrong.

The deeper risk is that these processes may not merely develop at different speeds, but may increasingly move in opposite directions.
Technological capability is accelerating at a historically unusual rate, while the formation of complementary human competence may be slowing, stagnating, or deteriorating in some areas.
At the same time, a large part of today’s professional competence is inherited capital:
knowledge accumulated over decades by engineers, technicians, doctors, operators, and other experienced specialists who are now progressively leaving the workforce through retirement.
Their departure does not simply reduce the number of workers.
It removes tacit knowledge, judgment, troubleshooting experience, and institutional memory that cannot be reproduced quickly through software, documentation, or formal education.
This makes the adjustment process far less controllable than a conventional technology-adoption delay.
In the late-1990s technology boom, one important problem was that investment and expectations moved faster than the economy’s ability to convert new technology into sustainable returns.
The present situation may contain the same mechanism, but with an additional force acting in the opposite direction:

technological capability may be accelerating precisely while parts of the human-capital stock required to absorb it are being depleted.

If these vectors continue to diverge, the resulting mismatch could be larger, more persistent, and economically more disruptive than a simple delay in technology diffusion.
The problem would no longer be that society needs time to catch up with its machines.

It would be that the machines are accelerating while part of the accumulated human competence required to understand, deploy, verify, and maintain them is simultaneously disappearing.

The most dangerous scenario is not:
“AI replaced the human.”
It is:
“Humanity created extraordinarily powerful technologies, but failed to reproduce enough of the human competence required to understand, deploy, verify, and control them.”

Selected sources

Stanford AI Index 2025 — Economy
Brynjolfsson, Rock & Syverson — The Productivity J-Curve
OECD — PISA 2022 Germany
KMK / IQB Bildungstrend 2022
NAEP Long-Term Trend 2023
World Economic Forum — Future of Jobs Report 2025
OECD — A Portrait of AI Adopters Across Countries
OECD — AI Diffusion and Complementary Assets
OECD — AI and Skills
Federal Reserve — Laurence H. Meyer, “What Happened to the New Economy?”

I think a massive short opportunity is brewing.

[/list]

When Technological Capability Outruns the System Around It

The problem may not be AI itself. The problem may be the condition of the system around it.

I want to draw attention not to a single isolated problem involving artificial intelligence, skills shortages, education, or economics. These topics are usually discussed separately. But when they are examined in isolation, the larger picture can be missed.
The real problem may be that society is entering the age of artificial intelligence after already accumulating a broad set of structural weaknesses: deteriorating educational outcomes, an aging professional core, industrial weakness, debt and fiscal pressure, and institutional overload.
At the same time, public attention and investment have become heavily concentrated on accelerating one variable above all others:

technological capability.

AI is not creating this crisis.
AI is entering an already stressed system.
Capital is being invested on a massive scale into the things that can be scaled fastest:

chips → data centers → models → software → automation

Meanwhile, other parts of the system are growing slowly, stagnating, or in some cases deteriorating.
This is why the problem is not simply that technological capital is advancing faster than human competence.
The picture is broader.
Technological capability may accelerate sharply while human competence, demographic stability, fiscal capacity, industrial capacity, organizational capability, and institutional capacity all come under simultaneous pressure.

These problems began before AI
This distinction matters because most of these problems began long before AI.
The aging of skilled professionals did not begin because of ChatGPT.
Problems in school education did not begin because of generative AI.
Europe’s demographic structure did not emerge because of AI.
The problems of Germany’s industrial model did not begin because of AI.
France’s public debt did not arise because of AI.
The structural budget deficit of the United States did not begin because of AI.
Humanity is therefore not beginning the AI revolution from an ideal starting point:

healthy society + new technology

It is beginning from something closer to:

already stressed system + extraordinary new technology

That is a fundamentally different situation.
Germany is simultaneously dealing with questions of productivity, shortages of skilled workers, education, population aging, administrative burden, and structural transformation.
France faces serious fiscal and debt constraints.
The United States faces a large structural budget deficit and a continuing increase in public debt.
These are three very different economies facing three different sets of problems.
But the important point is not to analyze each of them separately.
The important point is that all of these weaknesses can limit the ability of society to convert technological potential into real economic output.

AI is an engine. The economy is the entire machine.
Imagine AI as an extraordinarily powerful new engine.
Investors are looking primarily at the engine:

But an economy is not an engine.
It is the entire machine.
It requires:

engine + transmission + chassis + driver + fuel + roads + maintenance + financing + rules

AI is only one component of that system.
Even if it is the most revolutionary one.
If the engine becomes ten times more powerful, but bridges are aging, mechanics are becoming scarce, drivers are being trained less effectively, the company is heavily indebted, the electrical grid requires modernization, the bureaucracy cannot keep up, and the industrial base is losing competitiveness, then a tenfold increase in engine power does not imply a tenfold increase in the speed of the entire machine.

Where the investment shift may begin
This is where an investment shift may eventually emerge.
In the first stage, the market sees:

AI capability is exploding.

The conclusion appears obvious:
if AI can do more and more, then the potential value of the technology must be enormous.
This produces massive investment.
In the second stage, companies begin deploying AI at scale.
But gradually a gap appears between:

theoretical capability
and
realized economic output

Investors begin asking:

Why has the model become ten times better while the company has not become ten times more productive?

In the third stage, the answer may begin to shift from:

AI is not yet good enough

to:

the problem lies outside AI.

There is not enough competence.
There is not enough energy.
There is not enough infrastructure.
There is not enough capital.
There are not enough organizational structures.
There is not enough industrial capacity.
There are not enough people.
There is not enough time.
There is not enough institutional capacity for transformation.
This is where a real moment of recognition may occur.
Investors may discover that:

the marginal value of additional technological capability is being constrained by deterioration elsewhere in the system.

In other words, the next billion dollars invested in AI may no longer produce the same realized value because the bottleneck has moved.
In simplified form:

AI₁₀₀ + Society₄₀
may not be dramatically more productive than:
AI₇₀ + Society₄₀

The limiting factor is no longer AI.
At that point, capital may be forced to return its attention to areas that have been less fashionable for decades:

Investment logic itself may eventually redirect capital back toward the real economy and complementary capital.

The problem money cannot solve quickly
But there is a more difficult layer to this problem.
By the time markets recognize the imbalance, some of these capabilities may no longer be recoverable quickly with money.
Servers can be ordered relatively quickly.
But it is impossible to order:

100,000 engineers with twenty years of experience for delivery in eighteen months.

It is impossible to instantly buy a generation of skilled technicians.
It is impossible to instantly restore institutional memory.
It is impossible to rebuild the technological culture of an industrial organization in a short period.
It is impossible to purchase decades of operational experience.
It is impossible to instantly create teachers.
It is impossible to rebuild an apprenticeship system in a year.
It is impossible to quickly purchase engineering judgment.
Society may therefore recognize the misallocation of resources only after rebuilding complementary capital already requires decades.

We may have been living off accumulated competence
This is why aging specialists occupy a much more important place in the picture than it may initially appear.
For decades, societies may have been living off an accumulated stock while failing to notice problems in the flow that replenishes it.
A 50- or 60-year-old engineer today is the product of a school system from decades ago, technical education, decades of work, mentoring from an older generation, and a vast number of real breakdowns, mistakes, commissioning projects, and corrections.
That competence is capital accumulated over 30 or 40 years.
It may now be leaving the system much faster than society can create an equivalent replacement.
AI can create a dangerous illusion in this situation:

the shortage can simply be compensated for by technology.

Technology may help replace some functions of a specialist.
It does not necessarily replace the systemic judgment on which the operation of an entire organization depends.

AI as an amplifier — or as a mask
The same applies to education.
The important point is not to use deteriorating educational indicators as direct proof of a future shortage of engineers.
The more important observation is that the system responsible for reproducing human capital was already showing signs of weakness before the arrival of AI.
AI may then produce two opposite effects.
The first scenario is AI as an amplifier.
It accelerates learning and helps repair the problem.
The second scenario is AI as a mask.
It allows a person to produce acceptable output while possessing less internal competence.
The second scenario is especially dangerous.
Because statistically it may appear that:

productivity is fine.

But the underlying stock of competence may still be declining.

In other words, AI may temporarily conceal the deterioration of human capital.

This is not simply another Productivity J-Curve
Even the analogy with the dot-com era takes on a different meaning in this framework.
In the 2000s, complementary capital largely had to be created.
Today, some forms of complementary capital may have to be:

created + preserved + rebuilt

at the same time.
The older problem looked like this:

technology grows faster than complementary capital.

The new potential problem is different:

technology grows faster while parts of complementary capital are deteriorating.

This is no longer simply a Productivity J-Curve.

The central hypothesis
The central hypothesis is therefore not merely that AI develops faster than human competence.
It is this:

Quote

The AI revolution is not arriving in a system waiting to be transformed. It is arriving in societies already carrying accumulated structural weaknesses: aging professional populations, deteriorating educational outcomes in some areas, strained public finances, industrial restructuring, demographic pressure, infrastructure constraints, and institutions that adapt far more slowly than technology.

Quote

The central risk is therefore not merely that AI develops faster than human competence. It is that technological capability accelerates precisely while several of the complementary systems required to convert that capability into economic output are simultaneously weakening.

Quote

For a time, rapidly improving AI may obscure this imbalance. Eventually, however, investors may discover that the limiting factor is no longer the intelligence of the machine, but the condition of the society and economy in which that machine must operate.

This is why the central picture should not be imagined as only two lines:
technological capability and human competence.
It should be imagined as one line rising sharply:

Technological capability ↑↑↑

and several others:

[]Human competence
[]Education system
[]Industrial capacity
[]Fiscal capacity
[]Infrastructure
[]Institutional capacity
[/list]
Some of them grow slowly.
Some stagnate.
Some come under increasing pressure.

The central question

What happens when one component of civilization becomes exponentially more capable while much of the complementary system remains slow-moving, financially constrained, demographically stressed, or begins to deteriorate?

The problem is not AI.
The problem is the condition of the system around it.

And if attention remains focused only on the one technological variable rising fastest, society may fail to notice the deterioration of the broader system required to understand, deploy, finance, maintain, control, and ultimately convert that technology into real economic output.