Cyberabundance: When Artificial Intelligence Begins to Produce Knowledge

We have quickly grown used to seeing artificial intelligence produce text, images, video, software code, and music. A potentially far more significant transformation is now taking shape: AI is progressively taking part in the production of knowledge itself.

Researchers already use it to explore the scientific literature, analyze data, formulate hypotheses, program experiments, improve papers, and accelerate various stages of their work. Research into scientific automation goes further still, exploring systems capable of taking on a growing share of the research process itself.

The possibilities are considerable. Problems that previously required months of work could be explored much more quickly. Millions of publications can be analyzed and cross-referenced. Hypotheses that no one would have had time to test can be examined, while connections between disciplines can emerge at a scale barely accessible to a single researcher or even a human team.

This acceleration could, however, create a new situation. Our capacity to produce analyses, hypotheses, and eventually new knowledge could grow far faster than our human capacity to understand, verify, and integrate it.

We could thus enter an era of cyberabundance, where information becomes plentiful, intellectual production becomes extremely accessible, and potential knowledge multiplies at an unprecedented speed. In such an environment, true scarcity could progressively shift toward something else: trust.

Science rests on a far more demanding mechanism than the production of an idea. A hypothesis must be open to examination, a method must be understood, results must be verified, challenged, and, where possible, reproduced. A scientific conclusion acquires its value precisely because it goes through a process that progressively raises the level of confidence we can place in it.

Artificial intelligence could profoundly alter the balance between production and verification. Imagine systems capable of scanning thousands of papers, proposing hundreds of hypotheses, building models, generating code, analyzing data, and writing up their conclusions in a matter of hours. Even if a significant share of that output is relevant, it will still be necessary to determine which conclusions genuinely deserve our trust.

The difficulty stems from a fundamental asymmetry: human verification capacity does not advance at the same speed as computational generation capacity. A machine can produce a thousand additional analyses without our being able to instantly create a thousand experts capable of examining them with the same rigor.

This tension is already appearing in the scientific world, where some observers are beginning to consider the consequences of scientific output significantly amplified by automation. Traditional evaluation mechanisms were designed in an environment where producing research demanded enormous time and resources. If the marginal cost of generating a hypothesis, an analysis, or a paper drops sharply, validation mechanisms will also have to evolve.

We could then witness a historic inversion. Much of scientific development has been marked by the difficulty of producing and disseminating enough knowledge. In an environment of cyberabundance, the challenge could progressively become determining which knowledge deserves our attention, which has been sufficiently verified, and which can genuinely serve as the basis for a decision.

This inflation of intellectual output will extend far beyond universities. A company could ask several agents to analyze a market simultaneously and receive hundreds of strategic scenarios. A legal department could generate numerous interpretations of a regulatory change. A cybersecurity team could receive thousands of recommendations, while a manufacturer could continuously produce new optimization leads.

The volume of available content would increase considerably without guaranteeing an equivalent increase in useful knowledge. When producing becomes easy, value shifts toward selection, contextualization, provenance, validation, and the ability to determine the level of trust that can be placed in each result.

This transformation also raises a deeply human question: who answers for what is produced? In the traditional scientific model, the authors of a publication do not only receive credit for their work. They also assume responsibility. They must be able to explain their methodology, defend their reasoning, respond to criticism, and correct their errors.

Artificial intelligence can take part in reasoning without being able to assume that responsibility in the human, professional, or institutional sense. That is why the scientific policies emerging around its use continue to place accuracy, integrity, and conclusions under the responsibility of researchers, while requiring sufficient transparency about the contribution of AI systems.

The debate over whether an artificial intelligence could be considered an author may thus mask a far more important question: is there still a person able to understand, defend, and take responsibility for what is presented as knowledge?

This question will quickly extend beyond the scientific world. An executive receiving a strategic recommendation produced by several agents will have to determine how far they must understand the reasoning before applying it. An engineer will have to retain enough understanding to approve a design largely developed by a machine. In many professions, systems will be able to participate in a growing share of the reasoning while responsibility for certain decisions remains human.

We will therefore be able to delegate a growing share of intellectual work. The responsibility that accompanies certain decisions will remain far harder to delegate. This evolution could also lead us to speak of synthetic knowledge. We already use the expression “synthetic content” to describe artificially generated text, images, voices, or video. As multiple systems take part in research and reasoning, some knowledge may likewise result from largely automated chains.

A hypothesis could be proposed by one AI, developed by a second, tested by a third, compared against the scientific literature by a fourth, and finally presented to a person for validation. The result could be perfectly valid. Its intellectual origin would simply differ from the one we traditionally know.

A machine's contribution should not in itself invalidate a piece of knowledge. A discovery remains valid when it withstands appropriate validation methods. Its provenance, however, becomes far more important.

Which systems took part in the reasoning? Which data was used? Which steps were carried out by humans? Which assumptions were introduced? Which results were verified? Which model versions took part in the process? Which sources were consulted, and which of them genuinely carried authority?

Traceability could thus become to knowledge what chain of custody represents for certain kinds of evidence: a way of understanding where it comes from, which stages it passed through, and what transformations took place before it was presented to us.

This question becomes even more important when artificial intelligences begin to use knowledge that other artificial intelligences themselves helped create.

Scientific publications are progressively becoming sources consulted by AI systems. If a growing share of those publications is itself produced with their assistance, loops can appear in which machines work from knowledge already interpreted, reformulated, or generated by other machines.

This phenomenon does not automatically lead to a degradation of knowledge. It does, however, considerably increase the value of provenance and of preserving original sources. Research on the recursive training of models has already shown how important it is to preserve access to original data and to distinguish synthetic data when several generations of systems reuse artificially produced content.

Transposed to the world of knowledge, the principle deserves particular attention. A claim generated by an AI can be picked up in a document, summarized by another, integrated into a knowledge base, and then used by several agents. An initially minor error could then circulate long enough to progressively acquire the appearance of an established fact.

In an environment of cyberabundance, repeating a piece of information should never automatically grant it more authority. It becomes essential to be able to trace it back to its source, distinguish a primary source from its many derivations, and understand the chain of transformations that produced the information ultimately used.

This transformation should not, however, be viewed solely through the lens of risk. Artificial intelligence could unlock a considerable amount of scientific and intellectual capacity.

Researchers devote enormous time to searching for publications, cleaning data, programming, documenting, reformatting, preparing charts, and carrying out many other activities essential to their work. Some of these tasks can be accelerated, potentially giving humans more time to choose the problems worth studying, ask original questions, connect different disciplines, understand context, design relevant experiments, and exercise their judgment.

The researcher's profession could thus move up a level. Part of the human value would shift from the manual production of each element of the work toward the ability to steer, understand, challenge, and validate a far more abundant intellectual output.

This transformation could appear across a multitude of knowledge-based professions. An analyst would no longer necessarily have to personally produce every possible analysis, but would need to know which ones to request, which to compare, and which to explore in depth. An executive could have dozens of scenarios at hand without being able to delegate the responsibility of choosing the one that genuinely matches the organization's objectives and constraints.

The paradox of scale ultimately leads to another development: if machines produce more knowledge than humans can verify, we will probably use machines to help us verify the machines.

We can imagine architectures in which one AI formulates a hypothesis, a second actively searches for evidence that might contradict it, a third checks the references, and a fourth examines the methodology before a synthesis is presented to a human. Some systems can also assist in the search for evidence within a scientific literature that has become far too vast to review manually.

The objective would not be to eliminate human judgment. It would be to concentrate that judgment where it adds the most value. Machines could absorb part of the scale while humans retain the responsibility of setting the rules, assessing the important cases, interpreting context, and owning the decisions.

Such an architecture obviously introduces new governance questions. Which system verifies which other system? Are the models used for validation sufficiently independent of those producing the results? Which sources carry authority? How is a disagreement between several agents handled? At what point does human validation become mandatory? And above all, who remains responsible for the final result? These questions show why AI Governance alone will probably not be enough. A second discipline is progressively becoming just as important: Knowledge Governance.

The governance of artificial intelligence seeks, among other things, to determine which systems are used, which data is accessible to them, which actions they can perform, which risks they introduce, and under whose authority they operate. Knowledge governance focuses on what circulates between humans and these systems and on what the organization considers reliable enough to serve as the foundation for its activities.

When an organization uses artificial intelligence on a large scale, a growing share of its knowledge may be summarized, reformulated, interpreted, enriched, or generated by artificial systems. It then becomes necessary to determine which information constitutes the official reference, which sources carry authority, who validated a conclusion, when it was last verified, and whether it comes from a human, an AI, or a hybrid process.

It is also necessary to be able to identify documents that have become obsolete, determine which claims can be used automatically by agents, and recognize those that require validation before triggering a decision or an action.

These questions bear directly on organizations' readiness for artificial intelligence. A company may hold thirty years of documents, procedures, emails, databases, and tacit knowledge. Connecting an AI to that body of material does not automatically make the organization smarter. If its knowledge is contradictory, obsolete, poorly structured, or lacking clearly defined sources of authority, artificial intelligence may simply automate and accelerate that confusion.

This is precisely one of the challenges addressed by the Qb Knowledge Standard – AI Readiness & Knowledge Governance. Before allowing artificial intelligences and agents to make extensive use of an organization's knowledge, that knowledge must become sufficiently structured, traceable, and governed for humans and machines alike to determine what they can genuinely rely on.

In an environment of cyberabundance, this capability could become a major organizational advantage. When everyone can produce more analyses, summaries, and hypotheses, differentiation will shift toward the ability to maintain knowledge that is reliable, contextualized, traceable, and sufficiently governed to be used in decisions and by autonomous systems.

Artificial intelligence could give scientific research and organizations an unprecedented capacity for intellectual production. We will be able to explore more hypotheses, analyze more data, connect more knowledge, and accelerate certain discoveries. Work that previously required weeks or months will be achievable far more quickly.

This abundance will, however, change the nature of the problem. When producing an analysis becomes easy, verifying its quality gains value. When generating a hypothesis becomes almost instantaneous, choosing the one worth exploring becomes a skill. When millions of documents can be synthesized in seconds, knowing the provenance and reliability of the information used becomes essential.

And when machines begin to take part in the creation of knowledge itself, maintaining humans capable of understanding, challenging, validating, and taking responsibility for that knowledge becomes fundamental.

We could thus enter a period in which one of our main difficulties will no longer be accessing knowledge, but governing its abundance. Trust will then have to be built from provenance, traceability, source quality, validation methods, and responsibility clearly assigned to the people and organizations that use that knowledge.

This transformation already concerns scientific research. It will progressively affect companies, governments, and virtually every knowledge-based profession. The more artificial intelligence systems become capable of reading, analyzing, interpreting, generating, and sharing knowledge, the more Knowledge Governance will become an essential component of organizational architecture.

For Quantum Beyond, this evolution reinforces a central conviction: preparing an organization for artificial intelligence requires more than providing it with high-performing tools. Its knowledge must be structured, its sources of authority identified, its provenance preserved, the transformations it undergoes governed, and humans maintained who are capable of understanding and judging what the machines produce.

Artificial intelligence could allow us to produce and explore an amount of knowledge that previous generations could scarcely imagine. Cyberabundance is, in that respect, a tremendous opportunity. Our next challenge will be to develop the trust and governance mechanisms capable of turning that abundance into real understanding, informed decisions, and useful progress. Producing more knowledge will be a remarkable advance. Genuinely becoming more knowledgeable because of it will be the real achievement.