A Hundred AI Assistants, But Where Are the Great Scientific Works?

Every month seems to bring another artificial intelligence platform that can summarize papers, draft paragraphs, run calculations, and generate research outlines in seconds. For working academics, the convenience is real: tasks that once consumed an afternoon now take minutes. But a provocative new commentary published in the journal AI & Society asks an uncomfortable question about this abundance. If researchers now command what amounts to a hundred tireless assistants, why have the great, field-altering works of science not arrived in anything like the proportion one might expect? The piece, written by Seto Herwandito of Satya Wacana Christian University and Pramana of Universitas Sebelas Maret, appears in the journal’s Curmudgeon Corner, a column devoted to opinionated reflections on technology and society, and it argues that the very efficiency AI provides may be quietly starving science of the conditions under which breakthroughs are born.

The authors open with a historical contrast that has already struck a chord with readers. As Walter Isaacson describes in his 2007 biography of Albert Einstein, the physicist worked largely alone, with a blackboard, long stretches of uninterrupted thought, and very few external aids. The papers that continue to shape physics emerged from those constrained, unhurried conditions. Einstein’s creative process was slow and often lonely, requiring months or years of sitting with a problem, discarding half-formed ideas, and returning to the same question again and again. Today, by contrast, an academic can summon hundreds of computational helpers at once. These systems calculate, retrieve, rewrite, suggest next steps, and even propose titles and structure arguments. The cost of producing text, tables, and literature reviews has collapsed. Yet the volume of papers, the speed of review cycles, the density of citation networks, and the length of curricula vitae have all grown without a matching surge in works that reorient entire disciplines.

At the heart of the commentary is a distinction the authors keep returning to: AI multiplies the number of assistants, but it does not multiply the capacity for sustained, risky, and inefficient thinking. The technology lowers the cost of being busy. It has not lowered the cost of becoming a genius, or even of protecting the conditions under which difficult thinking sometimes occurs. A researcher can now generate drafts, run analyses, fill notebooks, and produce polished texts at industrial speed. But the willingness to stay with one difficult, possibly fruitless question for a decade remains an expensive proposition. It demands time that looks unproductive, the tolerance to appear slow while everyone else publishes, and institutional space that permits that appearance of idleness. In the authors’ framing, the new tools have made visible productivity easier than ever, and that is precisely the problem.

The commentary reaches back to an earlier technological anxiety to sharpen its point. Norbert Wiener, in his 1961 book Cybernetics, warned that the real danger of automation was not that machines would become too intelligent, but that humans would become too willing to hand over the slow, uncertain parts of thinking. Herwandito and Pramana argue that the current incentive structure proves Wiener right in a way he could not have foreseen. Universities and funding bodies reward productivity, and AI makes productivity trivially easy to demonstrate. The rational response for most researchers is to use the new assistants to clear many small questions quickly rather than to guard the slow, uncertain work of one large question. A hundred researchers, each equipped with a hundred AI helpers, can solve a thousand modest problems in a year. The same group, if it chose, could spend ten years solving a single unreasonable problem. Almost everything in the current system, the authors contend, pushes them toward the first option.

The tension is visible in the everyday habits of working scholars. The authors describe seeing it in their own working lives and in the practices of younger colleagues. The tools are genuinely helpful for clearing routine tasks, and they have freed hours that could, in principle, be spent on harder thinking. In practice, those hours are usually filled with more production: another draft, another literature scan, another incremental paper. The result is an academic culture that feels increasingly efficient and increasingly thin. The community has become very good at generating outputs, the authors write, but less practiced in protecting the conditions under which something genuinely new might appear. The freed time does not flow toward depth; it flows toward volume.

Importantly, the commentary is not a call for a romantic return to chalk and isolation. The authors are explicit that these assistants are useful and that they remove drudgery that never had any intrinsic scientific value. If researchers insisted on it, the time saved by automation could be redirected toward deeper work. The problem, they argue, is that nobody insists. The saved time is spent producing more of the same type of work, only faster. The system rewards that choice, and the tools make it almost effortless to comply. What looks like a technological revolution, in this reading, is functioning as an accelerant for the existing incentive structure rather than a challenge to it.

The authors do offer a path forward, though it is a demanding one. Great works may still appear, they suggest, from people who treat AI the way earlier generations treated libraries or calculating machines: as support for a long, stubborn conversation with a problem that refuses easy answers. That would require a deliberate decision to keep some of the old inefficiency, protecting stretches of time in which nothing publishable is produced, and judging the value of that time by the quality of the questions rather than the quantity of the output. It would also require institutions willing to recognize and reward that kind of patience, a significant ask in a sector increasingly measured by grant income, publication counts, and throughput metrics.

The piece appears under the banner of Curmudgeon Corner, which the journal’s editor describes as a short opinionated column on trends in technology, arts, science, and society, commenting on issues of concern to the research community and the wider public. The column’s framing question is stark: what is it to be human in the age of the AI machine? The editors note that while the drive toward super-human intelligence promises benefits to society, it also raises deep concerns, including existential risk, making an ongoing conversation between technology and society essential. Herwandito and Pramana’s contribution fits squarely in that tradition, using a personal, letter-to-the-editor format to raise a question that quantitative studies of AI-assisted science have so far struggled to answer directly.

Whether the commentary’s diagnosis proves correct is an empirical question that will take years to settle. Historians of science have long debated whether breakthroughs cluster in conditions of constraint or abundance, and the present moment offers an unprecedented natural experiment: an entire research ecosystem suddenly equipped with generative tools, its output measurable in real time. If the authors are right, the telltale sign will not be in the publication statistics, which will almost certainly keep climbing, but in the absence of the rare papers that redirect whole fields. Until such choices about how to spend freed attention become more common, the authors conclude, no one should be surprised that the blackboards of the past cast longer shadows than the crowded dashboards of the present. Humanity has given itself more hands to work with, they write, but has not yet figured out how to give itself more depth.

Subject of Research: The impact of AI research assistants on the conditions for major scientific breakthroughs in academia

Article Title: Einstein had a blackboard. We have a hundred assistants. Where are the great works?

Article References: Herwandito, S., & Pramana (2026). Einstein had a blackboard. We have a hundred assistants. Where are the great works?. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03358-2

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03358-2

Keywords: artificial intelligence, academic research, scientific breakthroughs, AI & Society, research productivity, Albert Einstein, Norbert Wiener, cybernetics, academia, creativity, research incentives, generative AI