The Renaissance offers a useful way to think about AI, particularly through the relationship between inquiry, authority, and inherited knowledge. Renaissance humanists brought renewed scrutiny to influential texts, examining their language and comparing their claims. Their work sometimes challenged much more than an interpretation. It questioned the foundations on which established authority rested.
Lorenzo Valla provides a revealing example. In the fifteenth century, he used linguistic and historical analysis to expose the Donation of Constantine as a forgery. The document had supported important claims to authority, but its standing did not protect it from examination. Valla’s work belonged to a wider intellectual movement that recovered ancient thought while also questioning accepted interpretations. The Renaissance did not invent reason or replace an entirely unquestioning past. It helped develop new ways to examine what had been inherited.
I see a possible parallel with AI. Beyond helping us retrieve information or complete research more quickly, AI could expand the practical ability to question an explanation, investigate its foundations, and develop an alternative. Someone who previously lacked the means to pursue a question might gain enough support to make a contribution that deserves serious consideration.
That possibility raises a deeper question about authority. If credible understanding can emerge from a wider range of people, will our institutions become better at recognizing it? Or will the ability to investigate spread faster than the opportunity to have that work evaluated and used?
We cannot predict the answer. We can, however, explore a plausible pathway through which expanded inquiry creates pressure on the arrangements that determine whose knowledge receives attention. To make that possibility concrete, I use a Possibility Chain: a seven-year sequence of causally linked forces showing how one development could create the conditions for the next. It is not a forecast, but a disciplined way to explore how expanded inquiry could gradually put pressure on the authority structures around knowledge. I have placed that pathway between 2025 and 2031. The dates are markers rather than a timetable: the opening forces reflect existing constraints and emerging capabilities, while the later forces describe developments that could follow under particular conditions.
The pathway depends on more than increasingly capable AI. People would need to use that capability to investigate, some of their findings would need to survive independent scrutiny, and institutions would need a reason to reconsider how they recognize contributions. Each development creates the pressure for the next.
2025 — Expert Shortages Slow Research
The starting constraint is the time and expertise required to investigate a question properly. Before someone can challenge an accepted explanation, they need to understand the work behind it. As knowledge accumulates, that preparation can become more demanding. In his research on the “burden of knowledge,” economist Benjamin Jones described how increasing knowledge can lead to longer preparation, narrower specialization, and greater reliance on teams. His evidence concerned patterns in invention and did not establish a universal shortage of researchers. It helps explain why access to the right combination of expertise can constrain inquiry.
In this pathway, “expert shortages” means insufficient specialist attention for the questions people could investigate. A small team may understand its own field well but lack the time or support needed to examine a connection with another discipline. An unexplained observation may remain unresolved because pursuing it requires knowledge the team cannot readily assemble.
The consequence is that some questions receive less examination than they warrant. An inherited explanation may remain in use because the people encountering its limits cannot yet develop a sufficiently strong alternative. That creates demand for tools that extend the reach of available expertise.
2026 — Research Tools Extend Expertise
AI research tools offer a way to ease that constraint within existing research arrangements. They can help scientists navigate unfamiliar literature and develop candidate explanations for further examination. Google’s AI co-scientist, introduced in 2025, was designed around this assistive role: scientists supply research objectives and can guide the system as it generates and refines hypotheses.
Research published in Nature in May 2026 provides a bounded demonstration. Co-Scientist helped develop biomedical hypotheses, with selected proposals assessed through laboratory experiments. The work supports the possibility of AI contributing to a research process that produces testable findings. It does not establish that generated proposals are generally reliable or that expert judgment is no longer necessary.
Within the pathway, researchers use this assistance to pursue questions that previously required more time or additional specialists. Existing teams remain responsible for priorities and evaluation. The tools extend what those teams can investigate without necessarily changing who holds authority.
This is an important distinction. Greater capability can be absorbed into an existing system. At this stage, the research institution may simply become more effective at doing what it already does.
2027 — Wider Inquiry Tests Assumptions
As research support improves, more assumptions could receive sustained examination. Investigators might compare accepted explanations with evidence from fields they previously lacked the capacity to explore. Some explanations would become stronger through that process. Others might prove reliable only under narrower conditions than previously understood.
Consider a hypothetical materials team investigating why a component repeatedly fails under conditions an established model predicts it should tolerate. AI assistance could help the team connect those observations with research from another specialty. That connection might suggest a different explanation and, crucially, an experiment capable of distinguishing between the two.
The pathway advances only if some alternatives withstand examination. A persuasive explanation generated by AI would not be enough. The work would need evidence that others can inspect and, where appropriate, reproduce.
Successful investigations could then attract collaborators and resources. The effect would extend beyond the individual finding: people would have a reason to support further inquiry by the team that produced it. That continuing support creates the conditions for research capability to develop beyond its original institutional setting.
2028 — Outsiders Produce Credible Research
The next development concerns whether people outside established research posts can sustain credible inquiry. Here, “outsiders” describes institutional position, not an absence of expertise. Some contributors might have substantial research experience but no current appointment at a university or major laboratory. Others might bring practical understanding of a problem that becomes more valuable when combined with specialist collaboration and AI assistance.
Building on independently checked results, some of these teams could gain enough support to choose their own questions and continue investigating. They might work with established laboratories for particular tests without becoming employees of those laboratories. Their methods and findings could remain available for examination even though no single institution directs the whole effort.
The structural change would be a continuing capacity to conduct research outside the positions that traditionally supported it. Our hypothetical materials team would no longer depend on being absorbed into a larger institution before pursuing its next question.
That would create a potential mismatch. The ability to produce useful understanding could become less closely aligned with the positions through which institutions expect to find it. Whether that mismatch matters would depend on the quality and recurrence of the contributions.
2029 — Credentials Miss Valid Research
As credible work emerges from these teams, research bodies could begin recognizing findings they initially overlooked. A contribution might receive little attention because its authors lack a familiar affiliation, only to gain credibility when another group reproduces the result. Repeated cases could reveal that established signals of expertise are missing some of the work institutions need to see.
There is existing evidence that an author’s standing can influence evaluation. A 2022 experiment published in PNAS sent the same research paper to reviewers while varying which author’s identity was disclosed: a Nobel laureate, a less-known researcher, or neither. The study found that author prominence affected review. It concerned a particular paper and setting, rather than proving that all disciplines respond in the same way, but it demonstrates how judgments can be influenced by information surrounding the work.
The force in this pathway is the growing recognition of missed merit. Credentials would remain relevant evidence of preparation and experience. They would become harder to treat as a sufficient guide to where useful contributions can originate.
This is where expanded inquiry could begin exerting pressure on knowledge authority. Institutions would face evidence that their ways of identifying credible contributors are not keeping pace with the changing distribution of capability. Missing that work could mean overlooking a discovery or delaying a solution they were seeking.
2030 — Independent Research Gets Equal Review
In response, some research bodies could change how independent work receives consideration. They might adopt explicit requirements that submissions be assessed through comparable processes regardless of whether the contributor holds an established research post. The aim would be to make documented methods and evidence more decisive in determining which work receives attention.
Equal review would not mean equal acceptance. An independent contribution would still need to withstand criticism, disclose its limitations, and meet the standards appropriate to the claim. The change would concern its opportunity to be examined seriously, rather than a presumption that unfamiliar contributors are more original or trustworthy.
For the materials team, this could create a dependable route into evaluation without first requiring a change in employment or affiliation. The institution would gain a better way to find useful work, while the team would gain an opportunity to establish the value of its contribution.
Authority would begin changing through these practical arrangements. Institutions could retain an important role, but more of their value might come from how well they evaluate and connect knowledge developed beyond their own boundaries. Their ability to recognize credible work would matter alongside their ability to originate it.
2031 — Costly Tests Limit Independent Research
Opening consideration would expose another constraint. More contributors seeking review could increase demand for independent testing faster than the capacity to provide it. Access to specialist attention or suitable facilities could become a decisive factor in which claims receive the evidence needed to establish their credibility.
Our materials team might now have its work taken seriously but still lack the resources to conduct a sufficiently demanding test. A larger organization could investigate several alternatives while the independent team could afford to examine only one. The opportunity to contribute would have broadened without producing an equal ability to establish a result.
This is the new burden created by expanded inquiry. AI could reduce the effort required to develop a promising explanation more quickly than it reduces the cost of determining whether that explanation holds. The constraint would shift from the ability to formulate an investigation toward the ability to complete a trustworthy evaluation.
The pathway therefore ends with an unresolved tension. More people could challenge inherited knowledge, while those able to provide or finance credible tests retain substantial influence over which contributions gain standing. Knowledge authority might become more open in one respect and remain concentrated in another.
Whether inquiry changes authority remains a human question
The significance of this pathway lies in the connection between expanded capability and institutional response. AI could help people investigate more without changing the authority structure around knowledge. Established institutions could absorb the additional capacity and continue operating much as before. A deeper change would become plausible when credible contributions repeatedly emerge in ways that inherited arrangements struggle to recognize and use.
Even that would not, on its own, establish a civilization-wide systemic transition. It could represent a consequential change within the existing human system, or help create conditions for a broader reorganization. The distinction matters because more capacity does not automatically mean a different organizing logic.
There is also a plausible branch in which inquiry weakens. People could use AI to obtain conclusions they no longer examine. A 2025 survey of 319 knowledge workers found that higher confidence in generative AI was associated with less reported critical thinking. The study relied on self-reports and does not prove a general decline in reasoning, but it cautions against assuming that access to capable tools necessarily produces greater intellectual independence.
That is the central tension in the Renaissance comparison. AI could help us examine inherited knowledge more rigorously, or become another source whose authority we accept too readily. The difference would depend partly on the habits we develop and partly on whether institutions make serious inquiry possible beyond familiar circles.
The choices are already worth considering. We can judge AI research tools by how well they help people investigate and check an explanation, rather than only by how quickly they supply one. We can examine whether promising contributions receive attention on their merits and whether independent testing is accessible to the people whose work requires it. These choices influence who can participate before any larger shift in authority becomes settled.
The possibility that interests me most is a person gaining the means to pursue a question they previously had to leave unanswered. That person might discover that the inherited explanation was sound. They might reveal its limits or develop a better one. What matters is whether they can conduct the inquiry properly and have the resulting work judged fairly. If AI expands that opportunity, its influence could reach well beyond the production of knowledge into the relationship between people and the institutions that decide what deserves to be believed.
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