Scientific and technological progress is the source of most of the comforts of modern life: sanitation, electricity, antibiotics, flight, digital media. If we believe the leading technologists of the day,1 much more is coming. When we picture that next wave, our instinct is to want it fast: a cure for cancer would be better today than tomorrow. This need for speed pushes our innovation systems to produce as much, as quickly, as they can.

But the affordances that enable speed—more capital, more labor, more compute—are not only an accelerator. They are also a filter. When timelines compress, even if only in our minds,2 we do not simply get the same discoveries sooner. Compression changes which kinds of work get pursued at all.

True, endeavors that optimize, refine, scale, or execute against a clear, achievable objective often benefit from compressed timelines. But work that is truly exploratory by nature usually does not. Why that is so is the subject of this three-part series. We begin with time, and with a claim that sounds strange at first: time only looks like a line in hindsight. When viewed looking forward, it is instead a surface.

The Hidden Shape of Time

We tend to picture time as a straight line, running far forward into the future and deeply back into the past. A week is seven days laid end to end. A month is thirty more of the same. A task with no hard deadline takes the same seven days whether we start it this week or next. Time, in this picture, lives in a single dimension, and one stretch is interchangeable with another of equal length.3

That picture is incomplete. It is how time appears retrospectively, but not prospectively. To wit: trace any successful technoscientific discovery from its end back to its beginning, and it resolves into a clean line: this experiment, then that correction, then the result, as though events could only have run one way. But that is not how time looks as the work unfolds. Looking forward, every moment opens onto several possible next moves, and each of those onto several more. The future branches rather than stretches, and what hindsight flattens into a line began as surface, providing space for a widening tree of paths not yet taken.4

Generative potential lives on that surface, and a system that sees only the line will structurally suppress that potential. Understanding why requires examining two features of time that technoscience today largely overlooks: surface area and recursive accumulation.

The Surface Area of Time

Start with the line. If a researcher has four years instead of two, the one-dimensional picture says they get twice as many steps, and so twice as many chances. Twice the time, twice the opportunity.

Now look forward instead of back. At each step the work can branch: a result suggests, say, three directions worth trying, and whichever one is taken suggests three more.

Count the distinct paths through that tree and the arithmetic is clearly not linear. Each step through time roughly triples the number of routes. Four steps open 27 possible paths; eight steps open 2,187. The timeline doubled. The possible paths, meanwhile, exploded more than eightyfold.5 This is what we mean when we say time has surface area: extend the line and you do not get a longer line, you get a wider surface.

When the goal is incremental (an optimization or a refinement), that explosion of paths is a burden rather than a gift. It becomes a problem of exhaustive search, the kind Edison faced in his light-bulb trials: thousands of materials tested over nearly two years until one worked. In that mode, a sensible aim is to shrink the possibility space and avoid wasted attempts. But when the goal is radical discovery, more paths mean more room for the unexpected. The surface area is the point.6

Why does that room matter so much? Because of variance. The trouble with a short timeline is not mainly that it creates stress. It is that it shrinks the space in which variance can appear, and variance is where many of the most important discoveries come from. Fleming’s penicillin is the textbook case: a contaminated culture during the course of extended research turned an accident into a breakthrough. Bell Labs shows the same pattern in technology: the transistor, which made modern electronics possible, did not come from executing against a known target. It came from noticing anomalous behavior that no one had set out to find. More time means more of the tree explored: more anomalies noticed, more dead ends repurposed, more chance encounters, more recombination, more opportunities for a project to end somewhere other than where it began. Long-duration searches don’t guarantee anything, but they widen the space in which variance can surface.

Recursive Accumulation

This affordance of space is even more powerful than the math above suggests, because the prospective surface also fundamentally changes as you move across it. That is the second property, and it is why a researcher cannot simply explore the whole tree at once, even with unlimited resources. Edison could test many filaments in parallel, because each trial stood on its own. But early astronomers staring at blurry images could not know they had to “correct for atmospheric distortion” until they had first discovered that the atmosphere was the problem. Each step they took reshaped and expanded the future paths available.

In short, the branches ahead (i.e., the possibility space) depend on the path already taken. What is known, and what sits just within reach of the known, determine what can be attempted now, and that boundary moves as the work proceeds.

Take a different turn early and a different subtree opens: branches that were unreachable before become accessible, while other paths that once seemed open disappear entirely. The route already traveled does not just record where you have been. It defines where you can still go. Collapse the path back to its starting point, as a short timeline effectively does, and you do not get the same tree faster. You get a smaller tree.

Taken together, these two properties explain why compressing a timeline does not merely make discovery more stressful; it also shrinks the possibility space entirely.

Institutional Pressure

The problem is that our discovery institutions lean away from these properties rather than into them. Modern science and technology are built to compress time, to deliver the speed we ask for. Grant cycles, publication pressure, funding rounds, venture fund lifespans—these forces write that demand directly into the protocols of frontier work. The result: institutions are optimized to find the shortest path from a known start to an acceptable outcome, not the long, wandering path to the best one. These institutions are not choosing bad work. They’re solving a very particular problem—minimizing the distance to a result rather than maximizing the value of the result achieved.

Part of the mistake is a belief that high-variance work is just bad work with long odds—a waste of resources. Institutions, and the people they answer to, tell themselves they are open to tinkering and adventure. But waste is the one thing no one tolerates, and that distaste tends to win. The more a system demands milestones, forecasts, and near-term proof, the more it favors work whose path is already partly known. That is understandable. It also works against exactly the projects whose value emerges only through the doing.

High-variance work is work whose payoff is hard to see at the outset. It needs long horizons the way a plant needs water, light, and time. Consider Watson and Crick’s search for the structure of DNA. A fuller account is available here (it is worth reading), but serendipity and recursive accumulation showed up at least five times:

1. Watson only encountered the DNA problem because he attended a Naples conference outside his immediate research focus. Herman Kalckar invited him despite the fact that Watson was not meaningfully working with him.

2. Watson only heard Maurice Wilkins speak because J. T. Randall was too busy to give the scheduled talk and sent Wilkins instead. Wilkins’s talk got Watson interested in DNA and convinced him that X-ray crystallography might be the way to solve it.

3. Watson only made it to Cambridge because a few professors helped him work around the official purpose of his fellowship. He was supposed to be doing one kind of research, but he wanted to do something else. The system bent just enough to let him go.

4. At Cambridge, Watson happened to meet Francis Crick, whose strengths fit nicely with Watson’s weaknesses, and vice versa. Watson knew the biological stakes of the DNA problem; Crick understood the structural and chemical side far better.

5. Jerry Donohue happened to share an office with Watson and Crick at exactly the right time. Watson was relying on textbook diagrams that showed the wrong chemical forms of the DNA bases. Donohue knew they were probably wrong, told Watson so, and that correction opened the path to the right base-pairing structure.

None of these moments could have been scheduled. Watson couldn’t have known he needed to attend that Naples conference, or that Wilkins would be the one to speak, or that Donohue would be in the right office at the right time with the right expertise. Each branch opened only because the previous one had been taken, and each took time to traverse. Remove any one of them—compress the timeline, foreclose a path—and the double helix might have remained hidden for years longer.

This is what temporal surface area looks like in practice: not a predictable sequence of steps, but an accumulation of contingencies that only becomes legible in retrospect. Many of our exploratory institutions work well enough, perhaps “better than we deserve,” as Vannevar Bush might have said. But the irony is that we build our discovery institutions as if the retrospective view is the only one that matters. New institutions7 are needed.

Designing for Exploration

Many people say science needs more funding, especially after recent cuts, actual and threatened, in the United States. We agree, but we think it is only half the answer. Some science also needs protected duration, enough surface area for the unexpected to emerge. No amount of short-term funding can widen a search space that short timelines structurally constrain.

What would institutions built for that look like? A few principles come to mind:

First, they must not pre-destine the search, explicitly or implicitly. A search can be fixed in advance in two ways: in time and in direction. To pre-destine it in time is to prune the tree before the work really gets going. The obvious case is simple: an exploratory-first institution cannot cap its headline support at the usual two- or three-year term. But this limitation can also show up in subtler ways. Picture a researcher granted a ten-year, exploration-focused award by a foundation that is known to reorient its strategy every three years and that reserves the right to cancel annual payments at will. The researcher will plan for three years, not ten, and shape the work accordingly. (Direction can be pre-destined too, but we save that for a later essay).

Second, these institutions must let variance actually be explored. Explorers have to be free to follow their own sense of what is interesting as the work unfolds. That freedom requires concrete affordances: discretionary funds to pursue unexpected leads without re-justifying the budget, permission to pivot without triggering review processes, and protection from having to demonstrate progress against the original proposal. Without such mechanisms, giving researchers more time is like showing them a wider landscape but forbidding them from leaving the road. The surface area exists, but they cannot reach it.

Third, these institutions must provide explorers with ready access to expertise across domains. Real exploration naturally weaves in and out of disciplines as it unfolds. When work touches chemistry, then biology, then computational modeling, the explorer should be able to consult with chemists, biologists, and modelers without friction. Too often, though, institutions inadvertently steer searches back toward the competencies already housed within their walls, cutting off high-potential paths that require outside expertise. A well-designed exploratory institution actively connects its explorers to the right experts when the work demands it, rather than constraining the search to domains it already knows how to evaluate and support.

Finally, they must not be homogenous. There should be institutions of different temporal approaches, each using its own taste and judgment about which explorers will flourish within its environment. A healthy system does not put every project on the same clock. Some work should sprint. Some should compound over medium horizons. Some should be left to wander, because wandering is sometimes how breakthroughs begin.

Portfolio Approaches

As the last point suggests, we don’t believe every exploration should run on a long clock. Rather, the ecosystem as a whole should have a home for each kind. Individual institutions should be careful not to spread themselves across too many temporal styles at once;8 in aggregate, though, the range should be wide. Sizing that portfolio well is hard, and it will take experimentation, much of which will play out over generations. For orientation, it helps to note roughly how today’s allocations break down:

• About 9% of venture capital goes to pre-seed and seed-stage investments, the earliest and most speculative category.9

• About 10% of large technology companies’ revenue gets allocated to R&D.10

• About 25% of federal science support goes to basic or fundamental research.11

These figures are not strictly comparable, and as we have noted, only a fraction of each is genuinely coded for a long horizon. Still, they give a rough sense of where a balanced portfolio might land.

Venture capital differs from science funding in one useful respect: the sheer breadth of its allocators. That diversity lives on a higher layer of the system (see the innovation-layers post here). A healthy ecosystem needs many allocators with different theses, different diligence standards, and different ways of choosing across timescales, not only within them.

And while some fields lend themselves to long horizons more than others, there is important long-horizon work across all of them. Mathematics rightly receives substantial long-horizon support. Biology needs it too, but gets far less. This disparity doesn’t track any fundamental difference in how discovery works—variance needs time to surface in both domains. It reflects institutional bias about which kinds of exploratory work are treated as legitimate. A well-functioning ecosystem has to set aside room for explorers in any and all domains to pursue long-duration, high-variance work, and back the conditions under which surprise can survive.

Choosing Which Futures to Explore

The idea of temporal surface area points to something uncomfortable: we cannot simply accelerate our way to breakthroughs. Compression reshapes which futures are reachable at all. Time, in other words, is not just a constraint to be beaten. It is a generative substrate that determines the shape of the possible. The paths that open over five years are not slower versions of the one-year paths. They are different paths, with different anomalies and different accumulations along the way.

We rarely get to choose how fast technoscience moves. When we act as if we do, by pulling the levers nearest to hand—tighter milestones, a fixation on legible deliverables—we unintentionally shrink the set of futures we can reach. Today’s innovation ecosystem optimizes far more for efficiency than for discovery, favoring discoveries that arrive through quick and direct paths over those that require extended exploration. It is no surprise, then, that the technological leaps of the last forty years feel smaller, in their remaking of daily life, than those of the forty years before, when discovery had more room to use time, variance, and recursive accumulation.

Asking any single institution, let alone the whole ecosystem, to abandon accountability, milestones, timelines, and discipline would be both unrealistic and irresponsible. The evidence, though, is hard to ignore: when the full range of affordances for discovery collapses into one standard form, everyone loses. The point is not that technoscience should be slow, or that short timelines are always wrong. It is that temporal surface area is its own dimension of innovation infrastructure, yet it has been structurally suppressed. We have collectively optimized our systems for speed and efficiency while ignoring the geometry of the search space itself.

Time is not the only surface area that matters. In Part 2, we turn to boundaries: how walls both constrain and enable discovery. In Part 3, we look at goal setting, and how the way we set targets shapes what we can find. Together, these surface areas form a way of understanding why our innovation system produces the results it does, and how it might be redesigned to produce a broader array of discoveries.

For too long the conversation has focused on money and risk.12 Far too little attention has gone to the structures beneath them—the affordances, as we call them—that make different kinds of exploration, and therefore different kinds of discovery, possible. That is the low-hanging fruit, and it is ready to pick.

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Did generative wandering (from our essay here) ever have a home in our innovation ecosystems? Or is it just a romantic idea we are projecting onto the past that never existed?

Many years ago, a scientist who sensed they were onto something—but couldn’t yet say what—knew where to go for support. Similarly, an entrepreneur with a strong propensity to tinker (lacking credentials or markets for their imagined product) could secure funding. These explorers operated with long time horizons, followed open-ended questions, and answered for the quality of their journey rather than predetermined results.

Fast forward to today and the forces of modernization have completely remade the landscape. Professionalization brought rigor to areas where it was deeply needed. Accountability brought transparency and made it harder to abuse power. Financial discipline brought stewardship and channeled resources more efficiently. And broader access opened doors long closed by class, race, and gender, and made opportunity less arbitrary. These weren’t minor tweaks; they were essential fixes that made the institutions that govern our lives dramatically better, fairer, and more democratic for far more people.

But as these forces strengthened, they also drifted toward extremes, each improvement slowly displacing other institutional affordances without anyone noticing. No single change was dramatic, and each was defensible on its own terms. But step back and the cumulative effect becomes clear: the open-ended, patient conditions that once enabled generative wandering were systematically—if accidentally—eliminated.

The last refuges where these conditions held out didn’t look worth protecting. They looked like inefficiencies to be eliminated, so they were ‘fixed’ out of existence. As a result, generative wandering no longer has a place to call home.

This essay is about what we lost, how we lost it, and why.

When It Was Different

Wealthy patrons funded curiosity without formal deliverables. Early federal science policy trusted researchers to follow truth wherever it led. Corporate labs supported multi-decade horizons. Some of the affordances they offered were deliberate, while others emerged from circumstance.

To see what we lost, we need to see what once existed.

The Patronage Era

Darwin’s Beagle voyage is the canonical example. He sailed as an unpaid naturalist in 1831, his expenses covered by his father (who initially dismissed the trip as “a wild scheme” and a “useless undertaking”). There was no grant, milestone report, or required theory of change. Darwin roamed South America and the Pacific for five years, collecting thousands of specimens, often letting his mind wander over fundamental problems in his hammock at sea. He didn’t publish On the Origin of Species until 23 years after the voyage ended.

This patronage system provided abundant time and return horizons that were generational, not quarterly (if they existed at all). Joseph Priestley discovered oxygen in a laboratory built by his patron, Lord Shelburne, with no requirements beyond pursuing what was most interesting to him. Sir Joseph Banks personally financed the scientific team on James Cook’s first voyage purely to see what they might find.

Concentrated fortunes exist in every era, but these patrons were willing to wait decades and wanted their family names attached to discovery, not immediate dividends. Investable wealth met patience, enabled by Enlightenment prestige-seeking.

The Early University Research Model

After World War II, the U.S. invested heavily in a federally funded university research ecosystem, guided by Vannevar Bush’s 1945 report Science: The Endless Frontier. Bush argued that “basic research is performed without thought of practical ends” and that researchers “must be free to pursue truth wherever it may lead.”1

The funding structure matched the rhetoric. Proposals were shorter and less prescriptive than they are today, evaluated more on the researcher’s judgment and ideas than on detailed milestones, and support came with fewer interim constraints on how research time was used. Early-career faculty entered academia with independence to pursue their own research questions, operating in an environment where that freedom was less tightly constrained by grant cycles, early publication pressure, and the need to continuously demonstrate progress.2 A newly tenured scientist in the mid-20th century could pursue a big question for extended periods without the same expectation of continuous publication that defines academic success today.

Sputnik had shocked the nation into believing the Soviets were winning through superior science—that basic research, however abstract, was a matter of national survival. Behind these researchers stood a nation convinced that exploratory science would win the future.

The Golden Age of Industrial Labs

Mid-century corporate labs like Bell Labs and Xerox PARC offer another frame: researchers with multi-year runway and minimal milestone gates, funded by corporate balance sheets robust enough to absorb long-cycle R&D.

“How do you manage genius? You don’t,” Mervin Kelly, director of Bell Labs, is said to have remarked. He ran the place as an “institute of creative technology” and structured it to give researchers substantial autonomy—wide latitude to choose problems, minimal day-to-day supervision, and time to explore fundamental questions without immediate pressure for results. One newcomer, William Shockley, recalled that Kelly explicitly gave him freedom to choose fundamental research problems instead of assigned projects. New hires were sometimes told to spend six months in the library before deciding what to work on.3

The output: the transistor, precursors to the laser, Unix, the C programming language, information theory. The transistor took over a decade from invention (1947) to reshaping industry. Xerox PARC developed the graphical user interface in the early 1970s; it reached consumers with the Macintosh in 1984.4

Mid-century corporate labs ran on recycled profits, insulated from short-term market pressures, directed by leaders who believed fundamental research would eventually reshape their industries—and who wanted their labs remembered as peers of the great universities.

These three institutional forms look nothing alike on the surface. But their deep structure was identical: patient capital, long horizons, and trust in explorers to follow questions wherever they led.

The Four Horsemen of Modernity

What killed these systems? Ironically, the answer lies in genuine progress: four improvements that made institutions better in most ways, but systematically eliminated the conditions for generative wandering.

Professionalization

Before professionalization, expertise was unregulated and often dangerous. Barbers performed surgery, anyone could practice law, and engineers had no standard training, sometimes with fatal implications. The push to professionalize—to create distinct professions with clear standards, licensing requirements, and formal training—was about protecting the public from incompetence and fraud. Medicine became safer, bridges stopped collapsing, and institutions could trust that a credential meant something.

Professionalization replaced amateur improvisation with reliable competence and that had major, unambiguous benefits for society. But professionalization brought standardization, and standardization brought silos, credentialing, gatekeeping, and immobility.

Consider:

• In science, the number of academic disciplines multiplied, resulting in researchers building careers within tightly focused niches. “Stay in your lane” became implicit hiring and tenure advice. The volume of scientific publications has grown rapidly—one study estimates a doubling time of roughly 17 years—reflecting an ever-expanding and increasingly specialized research landscape. A biologist in 1950 was a biologist. By 2000, she was a molecular biologist specializing in RNA interference in C. elegans—and her grant proposals had better reflect that.5

• A 2016 study of 18,000 grant proposals found that the greater the degree of interdisciplinarity, the lower the probability of being funded. Wandering across boundaries—precisely the behavior that enables combinatorial discovery—became a career risk. The system selected for depth at the expense of breadth.6

Professionalization carved knowledge into territories, and no one minded the gaps it created.

Accountability

For most of institutional history, decisions happened behind closed doors. Government agencies spent with little scrutiny, corporations answered to insiders, and nonprofits operated on reputation alone. The push for accountability was a democratic achievement that delivered real social gains—power now needed to justify itself in terms the public could understand. Transparency replaced opacity. Metrics gave citizens, shareholders, and donors tools to evaluate performance, and corruption became harder to hide. The question “how do you know this is working?” became not just legitimate but obligatory.

But accountability brought metrics, and metrics became targets. Campbell’s law is unforgiving: “The more any quantitative social indicator is used for decision-making, the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the process it is intended to monitor.”7

Consider:

• The 1970s marked a turning point for science: it had to start proving its value to laypeople. And since laypeople can’t evaluate science on its own terms, that meant metrics—legible outputs that could stand in for judgment.8 Achieving this legibility and accountability led to an explosion of performance measures: publication counts, journal impact factors, citation indices, the h-index.

• Whereas open-ended, long-term grants were relatively common in earlier eras, by the late twentieth century, many grants had relatively short terms, annual reporting requirements, and increasingly formal expectations of progress. As a result, the average Principal Investigator now spends nearly half their time writing grants rather than doing science.9

• Many proposals now require detailed predictions of outcomes—including preliminary data that demonstrates partial success before funding is even received. Researchers must thus predict their discoveries in advance to fit the proposal—a contradiction, since the greatest discoveries are by definition unexpected.10

What can be easily and confidently measured gets funded. What isn’t legible gets starved.

Financialization

Capital used to flow on relationships and intuitive decision-making that often masked waste (or worse). Pension funds invested without discipline, endowments sat on assets with no strategy, and corporate cash sat idle, neither deployed nor returned to shareholders.

Starting in the 1960s, and gaining heavy steam in the 1980s, that changed. New theory and practices made capital allocation systematic. The Capital Asset Pricing Model codified risk-adjusted returns. Spreadsheets turned “how long until payback?” from a vague sense into a precise calculation. Database systems enabled benchmarking—you could now compare this fund to that index, this R&D project to that hurdle rate, with precision. If you couldn’t quantify your expected return and timeline, you couldn’t compete for capital.

This was responsible, and it delivered widely shared gains: more efficient markets, better-managed institutions, capital flowing toward demonstrated returns rather than connections.

But it came with a cost: once you can measure something systematically, you manage it systematically. Financialization brought timelines, and timelines crushed patience. The language of investment infiltrated science. Grant proposals began requiring “broader impacts” statements. Translational became a magic word—research that couldn’t articulate a path to application looked wasteful. Government agencies faced pressure to demonstrate outcomes on political timelines.

Consider:

• Over time, many researchers learned to frame curiosity as deliverables. Competitive funding systems increasingly rewarded work that looked feasible, legible, and likely to pay off on schedule, while protected time for open-ended inquiry eroded. The message many researchers absorbed was simple: it was no longer enough to say, “I’m working on something interesting.” You had to show, in advance, how it would pay off on a grant-friendly timetable.11

• Venture capital, with its 10-year fund lives, retreated from anything that couldn’t show traction in five to seven years—which ruled out most fundamental science. Corporate R&D budgets, once insulated, became line items to optimize; the share of corporate R&D devoted to research rather than development has fallen sharply.12

• The Bell Labs model became economically untenable. As one innovation practitioner noted, if you tried to do something like Bell Labs in a startup framework today, it would fail—the timeline is simply too long for modern capital structures.13

The slack that once absorbed long-cycle exploration didn’t migrate to new institutions. It evaporated.

Massification

Even more than at present, narrow channels used to control who got opportunity: elite institutions, old boys’ networks, inherited privilege. Access to professions, education, and funding was restricted by class, race, gender, and geography. The push to broaden access was about both justice and efficiency: if talent is broadly distributed but opportunity isn’t, society wastes human potential.

Massification delivered. Doors opened, gatekeepers weakened, and the talent pool expanded dramatically. The benefits of this are obvious, not just for those with new access to opportunities but for society as a whole.

But broader access came bundled with standardization, and standardization bred conformity. In science, with hundreds of applicants per grant, agencies developed explicit criteria and bureaucratic procedures to ensure fairness. Reviewers gravitated toward consensus picks—proposals that offend no one.

Consider:

• An analysis of grant proposal approvals found that if a single reviewer among four gave a proposal a negative score, the funding probability dropped from 55% to 25%. High-variance ideas get filtered out by reliably penalizing work that excites some people and worries others.14

• In surveys, researchers describe avoiding “plunging into the unknown” because competitive funding systems reward safer, more legible work. As one senior researcher put it, the result is “pre-fab houses, but no cathedrals.”15

Massification removed old barriers but introduced new ones—against the unconventional, the interdisciplinary, the slow-burning. Consensus demands the conventional.

How Generative Wandering Died

The Four Horsemen changed the selection pressures, and the system adapted through a web of interconnected forces:

The funding filter. Funding systems now select against wanderers. Proposals with high variance systematically get filtered out, as do those with significant interdisciplinary reach. Those who thrive are those who can articulate objectives ex ante, who can predict their discoveries before making them. The ones who can’t, or won’t, leave.

Disciplinary siloing. Professionalization carved knowledge into territories, each with its own journals, conferences, and hiring committees. Darwin could be geologist, zoologist, and theorist simultaneously. Today that breadth would make you unhireable in any single department. Yet breakthroughs often emerge from collisions between unrelated fields—precisely the behavior the system now punishes. The system rewards depth at the expense of breadth, and wandering rarely respects departmental boundaries.

Explorer homogeneity. Massification was supposed to bring more perspectives, but the standardization required to handle volume actually reinforced filters for a narrow path. The people who survive the pipeline to run labs—still older, disproportionately male, trained at a handful of elite institutions—share intuitions about what looks like promising science. They set the standards that systems now rigorously enforce, and they fund researchers who look like younger versions of themselves.16 Diverse exploration requires diverse explorers. We broadened access and narrowed output.

Self-censorship. Researchers internalized the constraints. A recent survey of career scientists found that 78% said they would make significant changes to their research program if they were not constrained by the demands of grant funding agencies, including pursuing more ambitious programs, pivoting to new topics, and testing hypotheses others see as unlikely to succeed. In effect, scientists stop imagining open-ended explorations because they know the system will not support them. Thus the censorship is absorbed, metabolized, made automatic. Early-career researchers, in particular, are pushed toward safer projects that can reliably generate papers rather than more ambitious work that may fail.17

These four mechanisms compound. Funding filters select for predictability; silos prevent the cross-pollination that sparks new questions; homogeneity ensures the same flavor of bets get placed again and again; and self-censorship internalizes and reinforces these trends.

Why It Matters

No committee convened. No report was commissioned. No one reviewed the evidence and concluded that generative wandering should end. It simply became collateral damage, eliminated as a byproduct of genuine progress.

The cost of that loss is clear; the historical record is unambiguous. The transistor wasn’t on anyone’s roadmap. Darwin didn’t set out to discover natural selection. Information theory emerged only because Claude Shannon was allowed to wander. These weren’t anomalies. They represent a consistent pattern: some breakthroughs emerge from exploration that couldn’t have been planned, because the destination didn’t yet exist.

This doesn’t mean all science requires generative wandering. Most doesn’t. Incremental progress, hypothesis-driven research, and milestone-gated development are important, and the system we’ve ended up with serves them reasonably well.

But we’ve systematically dismantled the conditions that made other kinds of exploration possible, as if they were inefficiencies rather than the source of important breakthroughs.

The good news: these conditions can be reshaped. Rebuilding doesn’t mean nostalgia for hereditary privilege or regulated monopolies. It means designing modern institutional forms around the structural conditions this piece has mapped—and defending them against the pressures that eroded them before.

This is possible. The question now is whether we have the courage and conviction to do it.

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“Exploring the technoscientific unknown also requires organizations to embrace a culture that holistically values all routes to technoscientific exploration: all the mechanisms of the technoscientific method, the finding of both questions and answers, and the seeking of surprise and the overturning of conventional wisdom.” — Venkatesh Narayanamurti and Jeffrey Y. Tsao1

If you grew up playing video games, you might have some experience with RPGs—games where you roam the unknown, unraveling the story and finding gear that unlocks new routes, quests, and achievements. Like science fiction and fantasy, the best RPGs invest in rich world-building—and, as Tolkien recommends,2 in a map that invites it.

To make the adventure interesting, the map often has numerous areas, each unique in climate, topography, flora, fauna, and inhabitants. Some regions require specific gear to reach. As you progress, the map expands, revealing areas hidden until certain stepping stones are reached. Finishing the game requires exhaustively exploring most of the map, optimizing your character and team along the way.

This, strangely enough, is an apt metaphor for innovation at a systems level. This post will explain why.

A concept without a name

Let’s rewind two hundred years. On the deck of the Beagle, a young Charles Darwin wandered, filling notebooks, collecting beetles, sketching coastlines, chasing curiosities that didn’t coalesce neatly. Some, including his father, saw him less as a serious investigator than a genial collector, inclined to idleness and wanderlust. The Beagle berth looked like a gentleman’s diversion rather than disciplined science. For years after his return, he kept wandering—through correspondence, pigeon breeding, and long walks—before publishing On the Origin of Species two decades later. What looked off-path in the moment became the path.

The impact of Darwin’s work is incalculable. Ideally, we’d have more of this kind of thing. What looked like an anti-path turned out to be a generative one. If we sought to replicate ‘it’, what would ‘it’ be?

There isn’t great existing nomenclature to describe this concept. It encapsulates:

• A search without searching.

• Accountability, yet without strict milestones or measurements.

• A challenge that rewards readiness to grow—in mind, taste, and character—over credentials displayed and badges awarded.

• Accomplishment that is unpredictable and unrelated to the first step taken.

• Off the beaten path.

• Filled with wonder, heightened sensitivity to stimuli, and open-mindedness.

The route, not the destination

Given the importance of this concept, we’re giving it a name: Generative Wandering. Here’s a working definition:

Generative Wandering is a structurally afforded3 journey of curiosity where the wanderer’s open‑minded, persistent, and taste‑guided exploration creates value in the traversal—absent a foreknown destination. It celebrates the route—the questions asked, stepping stones laid, pivots made—as the engine of eventual outcomes, and resists fixing destinations ex ante.

It rewards journey quality and wayfarer attributes: curiosity, openness, good taste, perseverance, the ability to pivot, and a willingness to meet new problems where they are. It requires active attentiveness, resolve paired with a loose grip, letting the path co-create the outcome, and pro-social porosity: letting others in, letting ideas cross-pollinate, and knowing when to lead versus when to follow. Outputs include new questions, unexpected linkages, reframed problems, and seeds of ideas that later prove essential.

Generative Wandering is not dilettantism, novelty-chasing, procrastination dressed as curiosity, or a refusal to commit. It is bounded by values and taste, anchored to a search for coherence, and judged by the rigor and cumulative traction of the trail. It remains accountable to the accumulating clarity of the work rather than pre-set outcomes and schedules.

Innovation as a map

Innovation, at system scale, can be modeled like a map. The terrain is not geography but possibility space—combinations of domain knowledge, methods, constraints, and maturity—populated by explorers with different structural affordances. We reveal only the regions we roam.

Two dynamics follow. First, fog of war is real: routine progress clears nearby squares, but some regions only render after unusual traversals: a reframing, a cross-pollination, an unexpected observation. Second, activity ebbs and flows. Funding cycles, market incentives, and institutional gatekeeping can leave vast regions dark even when opportunity is large.

Why do some regions get traffic while others languish? The answer lies in how explorers are equipped.

Exploring the entire map

In RPGs, you don’t reach the glacier lake in sandals. You need the right kit. Innovation is no different. In this context, “kit” means the bundle of structural affordances that shapes where you can go and how far you can roam: funding runway, decision rights, equipment and compute, team composition, rights to use results, and how progress is judged.

Innovation is exploration. Healthy systems send explorers across biomes, along ridgelines, and into fog. Without mindful intervention, exploration drifts toward clusters: dense footpaths where near-term returns are obvious, sparse tracks where even the questions remain ill-posed.

Why? Because different bundles unlock different terrain. Short cycles and fixed targets push toward incremental improvements. Cross-discipline teams with dependable resources and real-context exposure are the kit for translation—turning known ideas into working systems. Multi-year runway, open-ended mandates, and evaluation that rewards reframing and journey quality are the kit for the kind of discovery that only Generative Wandering unlocks.

What we read as preference is often just logistics. Scientists and entrepreneurs follow paths their kit enables, not from lack of curiosity, but because affordances for other regions are missing. Macro forces have shaped these affordances toward near-term, objective-bound travel: short fund lives, quarterly OKRs, milestone-gated grants, and governance that treats non-results as failure. Those choices carve highways through known corridors and leave whole territories covered in fog.

The most under-explored regions today only render under conditions of Generative Wandering—you coax them into view by taste-guided traversal, not brute force. History shows us what this looks like:

• Darwin’s Beagle voyage looked like aimless collecting, but that broad, uncommitted observation built the raw material from which his views on natural selection later crystallized.

• Fleming’s contaminated petri dish became penicillin only because his lab left the mess alone long enough for the anomaly to register.

• Cook’s looping Pacific voyages looked inefficient, but each recursive pass corrected prior maps and revealed coastlines that direct voyages would have missed.

• Federer played multiple sports into his teens—what looked like delayed specialization became the foundation for two decades of elite play and a distinctive style no coach could have prescribed.

These aren’t romantic detours. Generative Wandering is a precondition for certain discoveries, yet the bundles that enable it are scarce, which is why large parts of the map stay dim despite vast opportunity. If access is gated by kit, coverage depends on rebalancing the affordances we make available.

Why it matters

The cost is systemic. When most explorers stay on the same trails, surprise is traded for incrementalism. The frontier stagnates because we keep optimizing the map we can see while starving the one we can’t.

This would be a parlor critique if the stakes were small. We are navigating compounding crises: a warming climate, energy transition, AI capability and alignment, biosecurity. None will be solved by harvesting the same clearings faster. We need new discoveries that reset the constraints and widen the map.

The need for Generative Wandering goes beyond a technical prescription. It is a cultural lodestone: a dare to do big things in a way that invites surprise. Civilizations move when they marshal taste, talent, and treasure in pursuit of open-ended exploration. That stance generates its own flywheel: awe draws in explorers; risk appetite expands; institutions relearn how to celebrate questions as first-class outputs. The ambient culture shifts from “prove you know the destination” to “prove you’re laying a high-quality trail.”

The call to action is simple to say and hard to do. Rebalance your exploration. Carve out real runway for Generative Wandering. Build parallel pathways so ideas migrate without single-threaded gatekeepers. Equip teams with the institutional cover to follow surprise. Tell different stories, ones that make reframing and pivots legible. If we do, more fog will lift. If we don’t, we’ll keep walking the same paths and wonder why the horizon never moves.

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Project Aeon is an evergreen investment platform designed to fund ambitious scientists and their ideas for decades, not years. Through a hybrid structure combining nonprofit and for-profit arms, we provide what existing systems overwhelmingly do not: truly open-ended, long-horizon support for exploratory science with transformative, field-creating potential.

Our Design

Project Aeon exists to restore generative wandering to science by operating differently than nearly all other science funders:

• Funds people, not projects, and embraces pivots as new information unfolds.

• Provides a transparent pathway to 20 years of funding through tranching.

• Selects for capability, curiosity, overlookedness, and uniqueness.

• Uses a portfolio approach to risk and success.

• Chaperones emerging breakthroughs toward commercialization via VC-Studio-like support.

• Shared incentives (via scientist carry pool) reward collaboration and collective risk taking.

• Evergreen structure powers a perpetual flywheel of discovery.

In part, Project Aeon is distinct because it is not led by scientists, though it is heavily supported by them. It is led by risk capital practitioners, entrepreneurs, and early-stage technology builders. While we have deep respect for scientists, this DNA affects how Project Aeon will source, select, and support scientific exploration, blending the best of philanthropy with venture capital risk taking, portfolio building, and operational infrastructure.

Our Approach

We are building a four-legged stool to support the next generation of breakthrough scientific discovery:

1) Capital Management

• What It Is: A purpose-built, values-aligned funding engine designed around the two things status quo capital providers fail to underwrite: uncertainty and duration. A non-profit grant-making entity paired with a return-seeking vehicle—together enabling funding from ideation through commercialization and creating an evergreen flywheel.

• Why It Matters: We match the risk appetite of capital with the risk profile of research, and we integrate investment vehicles in a continuum along that spectrum. As a result, we can provide stable resources for scientists whose work begins more as a question than a plan, and we can continue to support them as their work evolves and matures.

• How It Works: Long-term, open-ended grants to scientists are made through a special purpose Donor Advised Fund (“DAF”), using established instruments providing rights to future monetizable assets. Follow-on investments in translatable technology are made through a return-seeking LP/GP structure. A single “fund manager” is bound by governance that protects long-term orientation, mission integrity, and a commitment to widely shared benefit.

2) Sourcing, Evaluation, and Selection

• What It Is: If discovery is nonlinear, then selection must be designed to discern the people most likely to navigate nonlinear terrain well. Our method identifies scientists with the right tools for trailblazing, validates their scientific capability through peers and mentors, and encourages them to pursue their biggest, boldest ideas.

• Why It Matters: Current funding systems predictably yield “safe excellence”: impressive work that extends existing paradigms but rarely breaks them. A portfolio-minded willingness to back exceptional people on uncertain paths, paired with disciplined thinking and feedback loops that improve judgment over time, has famously led to outsized results in other fields.

• How It Works: We solicit input from thesis advisors and department chairs to identify capable scientists with research interests aligned with our mission. A lightweight application and interview process evaluates their creativity, tenacity and intellectual honesty. Throughout, we use portfolio thinking to structure diverse cohorts and we aim to reduce the ways traditional selection processes accidentally narrow the field: prestige filters, consensus traps, metrics theater, and the subtle pressure to prioritize certainty over curiosity.

3) Community of Practice

• What It Is: A living network across science, media, policy, education, and future-minded technologists that normalizes patient, open-ended research and paradigm-shifting inquiry; a place where exemplars lend credibility, shared norms spread, and ambitious work becomes more socially survivable.

• Why It Matters: Community begets more community; signal strength attracts new signalers. Over time, the community will expand our pool of grantees and increase each scientist’s odds of success through access to an ever-expanding network of collaborators.

• How It Works: Initially, an online forum for discussion and collaboration, along with in-person events bringing small groups together to discuss concrete solutions to science funding challenges. Over time, gatherings of funding cohort members and larger events will promote knowledge sharing and cross-pollination of diverse lines of effort.

4) Internal Support System

• What It Is: This is the connective tissue that helps investee scientists navigate challenges, push through obstacles, and ultimately translate their research to real-world impact. In addition to financial support, we surround researchers with the tools, skills and resources to maximize their chances of success.

• Why It Matters: Most funders either micromanage or disengage; both models fail in predictable ways. Our goal is to increase the probability that courageous inquiry survives the early fragility period—when uncertainty, ambiguity, and institutional friction pose the greatest risk to success.

• How It Works: In the near term, the support system will provide logistical support to help researchers spend more time in the lab and less on administration. Over time, it will include resources related to prototyping and engineering, IP protection, company formation, and third-party fundraising. Still, we aim to stay lean—cultivating selective excellence at the support that matters most.

These ideas aren’t entirely new, but they lack committed manifestations. We borrow from Fast Grants, Donald Braben’s Venture Research, Bell Labs, and Y Combinator—among others—but Project Aeon is unique in its operations, hybrid form, evergreen structure, and genesis.

Our Objective

Project Aeon is principle-driven and low-ego. We practice cathedral thinking: we intend to do work today that future generations will thank us for, even if our names aren’t written on the cornerstone. We’re not trying to own breakthrough science, but to increase it—ethically, equitably, and at a cadence the world can feel. If we succeed, the next Darwin, Curie, or Planck will have the freedom to follow their curiosity sooner, with deeper support, and with a community that helps their discoveries reach the lives they’re meant to touch.

“Not all those who wander are lost …” – J.R.R. Tolkien

“The most exciting phrase to hear in science, the one that heralds new discoveries, is not ‘Eureka!’ (I found it!) but ‘That’s funny…’” – Isaac Asimov

Short-termism and objective orientation pervade our world. Still, many of us share the intuition that some pursuits need freedom from both to bloom: love, spirituality, philosophy, and great art.

What about science? The great discoveries of the past arose from wandering and wondering, patience and persistence, curiosity and creativity. Yet today, science is governed by strict milestones, rigid funding applications, enforced conformity and distrust accompanied by excessive monitoring.

What is lost in a world where science no longer has the freedom to wander?

Growing evidence reveals a troubling trend: discoveries that open pathways to unimaginably powerful new ideas are drying up. Heliocentrism. Evolution. Relativity. Quantum mechanics. Revolutions like these cannot bloom when we force scientists to abandon big ideas, compartmentalize research into milestone-driven fragments, and seek permission from the establishment.

Now is the time for action—not just words.

Restoring Generative Wandering

Project Aeon is an evergreen funding vehicle for scientific work that can’t be captured in rigid proposals, de-risked with preliminary results, or accomplished in short grant cycles.

Our ambition: fund 1,000 scientists to pursue world-changing, unorthodox ideas over long time horizons.

Our philosophy starts from a simple premise: discovery isn’t linear, and systems built for predictability suffocate the ideas that matter most. Trust must be our core technology. We exist to back wandering minds—people of deep curiosity and great capability—and give them the time and freedom their explorations require.

We’re one model among many that science needs, an exploration-first approach built on seven principles:

Back people, not rigid plans. We evaluate taste, tenacity, and judgment in practice: how candidates choose problems, adjust course, and reason from partial evidence. We focus on how candidates think and work, confirm with a few close collaborators, and decide quickly.

Design for open-endedness. Discovery isn’t linear. We give scientists multi-year freedom to explore, celebrating anomalies and detours. The goal is to enlarge the map, not pretend we know its contours before the exploration has begun.

Unearth the overlooked. Breakthroughs rarely arise from the center, and ability is widely distributed even if opportunity is not. We look beyond prestige, searching across geographies, institutions, disciplines, and career stages to surface unfashionable problems and overlooked talent. Past success isn’t a proxy for future originality.

Pair freedom with rigor. Freedom only works when paired with discipline. We value bold hypotheses tested with care, honest updates over theater, and flexibility in the face of new signals. Progress is measured by upgraded questions, closed dead ends, and conceptual advances, not tidy reports.

Aim science at world-changing, widely shared benefit. Too often, scientific advances serve the few. We back explorations that enhance human and planetary flourishing: strengthening public goods, repairing ecosystems, broadening access to resources, and building resilience.

Shepherd ideas from curiosity to real-world application. When promising signals emerge, we help ideas cross the valley of death, providing support for translation, commercialization, and access to downstream funders.

Steward capital for long-termism and regeneration. Our capital model is built for durability: a charitable engine anchoring long-horizon exploration, paired with a return-seeking vehicle for commercialization. Lean operations ensure dollars reach the lab. Returns are reinvested into new explorations, creating a virtuous cycle.

Project Aeon won’t fund every pathbreaking scientific exploration, but it will be a home for ambitious, non-conformist ideas and a bridge from open-ended inquiry to widely shared benefit.

Why Now—and How to Help

We stand at a hinge point in history. The crises before us—ecological, technological, institutional—are not incremental problems. They are system-level tests of human ingenuity. Meeting them will require science operating at its full creative potential: free, ambitious, and generative.

Over the last 75 years, the optimization mindset that made our world efficient has made scientific exploration timid. We’ve built a system that chases incremental answers instead of cultivating big new questions—one that elevates consensus over dissent. This pushes us further from the discoveries this century demands.

But a window is open. New funding models are emerging. Scientists are restless for change. The public is hungry to believe again that science can serve all of humanity, not just the lucky few.

Project Aeon exists for this moment. By reimagining science funding and stewardship, we can unlock a new era of unprecedented discovery equal to the scale of our challenges.

If you believe the next century deserves more than incremental progress, and that trust, time, and the freedom to wander must be restored to science in order to meet that goal, come help us.

Share this vision. Lend your voice, expertise, network or capital.

How can you start?

Follow one of the links below to find out more.

Links

Our website.

Our community.

Contact us.

Acknowledgements

Project Aeon is the result of a multi-year wandering into the deep, dark substrate of our inability to effectively tackle the greatest civilizational challenges of the late 20th and early 21st century. But we are not without guides in our wanderings. Below is a short list of people and organizations we are thankful for as torches lighting the way through the dark maze. Some are no longer with us and some have changed, but their influence endures. What follows is only a partial list (with apologies to those we’ve missed):

Emergent Ventures (we are especially grateful for their funding support)

Donald Braben

Thomas Kuhn

Kenneth Stanley

Fast Grants

Donald Stokes

Vannevar Bush

Venkatesh Narayanamurti

Jeff Tsao

MacArthur Foundation

Matt Clancy

Y Combinator

Michael Nielsen

Kanjun Qiu

Pierre Azoulay

Lux Capital

The Astera Institute

Arc Institute

DARPA

Bell Labs

Speculative Technologies

Howard Hughes Medical Institute

First Round Capital

Builders Vision

Renaissance Philanthropy