Perspective · 27 October 2026 · Bali, Indonesia

Where are we today, what do we still understand, and what might humanity forget over the next decade?

For decades, our industry has lived with technical debt. Now another form of debt is coming into focus: learning debt. AI is already changing how we work in Bali. The deeper question is whether people will keep acquiring the knowledge needed to understand, maintain and rebuild the systems they increasingly ask AI to operate. This is our October 2026 snapshot of that risk.

AI can accelerate a project. Understanding the technology keeps it maintainable. Talk to our engineers in Bali about your next software or automation project. Talk to our engineers.

For decades, software and hardware engineering have struggled with something called technical debt. Systems are developed under commercial pressure, with limited budgets, deadlines, compromises, and shortcuts. Not every problem gets fixed, not every architecture gets perfected, and not every security weakness gets eliminated.

In a free-market economy, technical debt is practically impossible to eliminate entirely. Commercial development rarely has unlimited time or funding, and businesses must balance engineering quality against economic reality. Martin Fowler’s explanation of technical debt describes how engineering compromises can make future work more costly.

Governments face a related but distinct trade-off: whether to disclose newly discovered vulnerabilities or retain knowledge of them for intelligence and national security purposes. The United States’ 2017 Vulnerabilities Equities Policy describes that balancing process. Retaining a vulnerability is not, by itself, the same thing as ordinary technical debt.

Technical debt has therefore never been purely a technological problem. It is fundamentally connected to economics, incentives, politics, and competing interests.

But in 2026, artificial intelligence is introducing a potentially much more consequential category of debt: learning debt, educational debt, and the gradual disappearance of human technical understanding.

And unlike technical debt, this new form of debt may not reside in machines, software, or infrastructure. It resides in people—or, more precisely, in the knowledge and skills that future generations may never acquire.

We belong to a generation of engineers and technologists who learned how things work from the ground up.

We studied how electrons and charge carriers behave inside the crystal lattice of silicon. From semiconductor physics, we progressed to diodes and transistors, then logic gates, integrated circuits, microcontrollers, CPUs, memory architectures, and bus systems.

We learned about machine instructions, microcode, assembly language, operating-system kernels, device drivers, networking, compilers, programming languages, and eventually the increasingly sophisticated abstraction layers of modern software architecture.

We understand the technological chain from the physical behavior of semiconductor materials to the software applications running on today's infrastructure.

Theoretically, we could even design our own processors, controllers, and memory systems. Manufacturing modern semiconductor components would, of course, require industrial capabilities far beyond an ordinary engineering organization.

But the important distinction is that we understand the principles and the layers connecting them.

This knowledge was acquired through years of education, experimentation, practical work, failures, debugging, and experience.

In our company in Bali, we have already integrated artificial intelligence throughout virtually our entire technology stack.

AI assists with hardware deployment, machine management, operating-system installations, infrastructure provisioning, system administration, software development, architecture, design, troubleshooting, and numerous other technical activities.

It has become an extraordinarily useful assistant, participating in work that previously required significant human effort.

We would certainly not want to return to working without it.

But there is one crucial distinction between our situation today and what may happen in the future.

If every AI service suddenly disappeared tomorrow, we could continue operating our infrastructure, maintaining our systems, writing software, and developing new products.

We might be slower. We might be less productive. Some activities would become more expensive.

But we would still know what we were doing.

We understand the underlying technologies. We understand the infrastructure. We understand the software. We understand the architecture and the decisions behind it.

At least within our organization, we have not yet accumulated the kind of AI-induced learning debt that would leave us unable to function independently.

This is the technological position we want to document on October 27, 2026.

We have developed and adopted an extraordinarily capable assistant.

But this assistant introduces two fundamental possibilities.

First, it may eventually become capable enough to perform most or even all of the work for which it currently assists us. At that point, it may no longer need us. Entire professional roles could disappear or change beyond recognition.

Second, and potentially more consequential, the next generation may have little economic or practical incentive to learn the foundations we spent decades acquiring.

Why study assembly language if AI writes the code?

Why understand operating-system internals if AI deploys and maintains the infrastructure?

Why learn programming if AI can design, implement, test, and deploy an application?

Why study how computers actually work if interacting with an AI assistant produces the desired result?

These are not arguments against AI.

They are questions about the long-term consequences of replacing the process of learning with the process of obtaining answers.

A 2026 conceptual paper by Margaret-Anne Storey distinguishes traditional technical debt from cognitive debt: the erosion of shared understanding as software evolves. It also describes intent debt, where the rationale behind design decisions is missing. This is a framework for thinking about software health, rather than proof of inevitable skill loss.

Our concern extends beyond software teams. What happens when entire educational systems, professional training programs, and successive generations stop developing foundational knowledge?

We do not need to speculate about every aspect of this transformation.

We can already observe changes among our own customers.

Tasks that previously required hiring a copywriter are now handled by AI.

Translations that once required a professional translator can increasingly be completed without one.

Routine software development that a customer might previously have outsourced to a developer in India can now be performed with AI assistance.

For many of these assignments, the customer simply no longer needs to commission the same external services.

This is our practical observation from Bali, not a claim that these professions have disappeared worldwide.

However, broader research supports the direction of change. A 2025 study published in Management Science found a 21% relative decline in postings for writing and coding jobs exposed to automation, compared with jobs requiring more manual-intensive skills, on a major online freelancing platform during the eight months following ChatGPT's introduction. Demirci, Hannane and Zhu

The consequences raise uncomfortable questions.

What happens to the people who previously earned their living performing this work?

What happens to the junior developer who needs practical assignments to gain experience?

What happens when companies no longer employ beginners because AI already performs much of the work traditionally assigned to them?

And perhaps most importantly: How do future experts acquire expertise if the traditional opportunities to learn are disappearing?

Artificial intelligence can undoubtedly be an exceptional educational tool.

It can explain complicated subjects, provide personalized instruction, answer questions patiently, and make knowledge accessible to people who previously lacked access to good teachers.

Research by Kestin and colleagues found improved learning outcomes with an AI tutor designed around established educational principles, in a university physics course.

But a high-school mathematics experiment by Bastani and colleagues found that students using an unrestricted AI interface performed better during practice but worse when the assistance was removed. A tutor designed with learning safeguards largely mitigated that effect.

The OECD's Digital Education Outlook 2026 explicitly identifies this distinction between completing tasks successfully and acquiring genuine knowledge. OECD

A similar concern emerged from a 2026 experiment involving 52 software developers: participants using AI assistance scored lower on subsequent comprehension tests than those who completed the programming exercise without AI. The study was relatively small, and it does not establish that long-term skill loss is inevitable. But it illustrates precisely the risk we are discussing. Anthropic

The critical question is not whether AI can teach.

The question is whether future generations will still be encouraged—or economically motivated—to learn.

There is an enormous difference between understanding a solution and merely receiving one.

Here in Bali, conversations about artificial intelligence increasingly move beyond software development and productivity.

People ask questions that, only a few years ago, might have sounded like science fiction.

Who actually controls artificial intelligence?

Who will control it tomorrow?

Will a handful of corporations determine which technologies, knowledge, and capabilities remain accessible?

Will governments use AI as an instrument of economic, technological, or geopolitical power?

If nations, companies, and individuals become dependent on AI systems they neither own nor understand, what happens when access is restricted, prices change, or those systems become unavailable?

And ultimately:

Does whoever controls AI control the world?

That question is deliberately provocative. Control of AI is not the same as control of the world. Nevertheless, the concentration of advanced AI development, computing infrastructure, semiconductor manufacturing, and model ownership is already a measurable concern.

Stanford's AI Index Report 2026 documents how notable AI model development remains concentrated in industry, with a small set of organizations accounting for a large share of releases. Open models and research also remain part of this evolving landscape. Stanford HAI, Research and Development

These are remarkable questions to hear in everyday conversations on a small island in Indonesia.

And they demonstrate how quickly AI has moved from a specialized technological discipline into an issue affecting society as a whole.

We are writing this in October 2026, not because we know what will happen, but precisely because we do not.

Today, we have an extraordinarily powerful technological assistant.

Today, we still understand the underlying systems.

Today, we can still continue our work without AI.

Today, we can still pass our knowledge to another generation.

But will that remain true?

Perhaps future generations will become more capable than ours because AI makes advanced education universally accessible.

Perhaps they will develop entirely new forms of technical understanding that we cannot yet imagine.

Or perhaps we are witnessing the beginning of a gradual erosion of fundamental knowledge, driven by a simple economic reality: Why invest years learning something that a machine can already do?

Technical debt has always been a commercial and engineering reality.

Learning debt could become a societal and generational problem.

And while technical debt can often be managed by investing more money and engineering effort, recovering knowledge that an entire generation never acquired may be considerably more difficult.

That is our concern.

Not that AI is becoming too intelligent.

But that humans may gradually lose the incentive, the opportunity, and eventually the ability to understand the technologies upon which their lives depend.

Let us return to this snapshot on October 27, 2027.

Then again in 2028, 2029, 2031, and 2036.

Let us ask what has changed.

Are we still developing independent technical capabilities?

Are young engineers still learning the foundations?

Do we still understand the systems we operate?

Have AI systems replaced more professions, or created entirely new ones?

Who controls the technology?

And if AI were suddenly switched off, could we still operate, maintain, and rebuild the technological world around us?

In October 2026, our answer—at least for our own organization—is still yes.

Let us see whether we can say the same thing one year from now.

Bali, Indonesia — October 27, 2026

The right technology partner should understand both the AI tools and the systems behind them. Bring your next software, infrastructure or automation project to our team in Bali, and let’s discuss how to build it with long-term understanding in mind. Discuss your next project.

The following publications provide background and research evidence for the article. They do not independently verify the firsthand observations about our company or customers in Bali.

Martin Fowler — Technical Debt (2019)
Explains the original technical-debt concept, its economic implications, and the consequences of engineering compromises.

Margaret-Anne Storey — From Technical Debt to Cognitive and Intent Debt (2026)
This conceptual paper argues that AI-assisted development introduces risks involving lost human understanding and missing design rationale.

OECD — Digital Education Outlook 2026
Examines AI, education, independent thinking, and the difference between completing tasks and actually learning.

Anthropic — How AI Assistance Impacts the Formation of Coding Skills (January 2026)
Experimental research examining whether developers acquire the same understanding when learning new programming concepts with AI assistance.

Bastani et al. — Generative AI Without Guardrails Can Harm Learning (2025)
Peer-reviewed research published in PNAS, demonstrating how unrestricted AI assistance can undermine independent mathematical performance.

Kestin et al. — AI Tutoring Outperforms In-Class Active Learning (2025)
A useful counterpoint: research demonstrating that appropriately designed AI tutoring can significantly improve learning outcomes.

Demirci, Hannane & Zhu — Who Is AI Replacing? (2025)
Published in Management Science. Examines how generative AI has affected demand for freelancers, particularly in writing, coding, and other digitally deliverable tasks.

International Labour Organization — Generative AI and Jobs: A 2025 Update
A global assessment of employment exposure to AI, distinguishing between the transformation of occupations and outright job replacement.

Stanford University — AI Index Report 2026
Comprehensive research on AI capabilities, adoption, industry concentration, infrastructure, national strategies, and global development.

White House — Vulnerabilities Equities Policy and Process (2017)
A historical government policy document illustrating how national-security interests can influence decisions about whether technological vulnerabilities are disclosed or retained. This is related to, but distinct from, ordinary technical debt.