The Waymo effect: how AI is quietly making research less collaborative

How frictionless technologies teach us to prefer our own company – and why research leaders should worry.

On a recent trip to San Francisco I did the thing that every visitor to San Francisco now does: I summoned a car with no one in it.

The Waymo arrived with the serene confidence of a machine that has never once worried about where to find parking, and I climbed into the back seat, glanced instinctively at the driver’s seat to say hello, and found myself nodding politely at an empty chair. The steering wheel turned itself. I have spent a reasonable portion of my life thinking about counterintuitive aspects of physics, but that did not help me with the mild existential vertigo of watching a steering wheel moving on its own – an unseen driver responsible for my safety.

I was in San Francisco, in part, to spend time with Susan Winslow, CEO of Macmillan Learning. We are colleagues within the Holtzbrinck group, and we had come together for the most human of professional reasons: to collaborate. To sit in the same room, compare notes on how AI is reshaping our respective corners of research and education, and do the kind of thinking that is stubbornly difficult to do over video calls.

And yet, comparing notes on our Waymo experiences, we discovered we agreed on something else entirely: the rides were wonderful. As two self-confessed introverts, we had each found the driverless car to be a small oasis. No obligation to make conversation. No silent negotiation over the radio. A guilt-free space to be alone with one's thoughts, finish an email, or take a call en route without the awkwardness about conducting it in front of a stranger. The car was quiet, smooth and entirely undemanding.

It took us slightly longer to name what we had lost. Two people who had crossed continents to talk to each other were quietly delighted by a technology whose central feature is that you don’t have to talk to anyone.

Naming the Waymo effect

Let me attempt a definition: the Waymo effect is what happens when a technology removes the friction of dealing with another human being, and we experience that removal as pure gain – because the costs of the friction were always visible to us, while its benefits were not.

This is a familiar move for anyone who has read Tim Wu on “the tyranny of convenience” – his argument that once a frictionless option exists, we take it by default, and quietly surrender whatever the friction was doing for us, since nobody advertised it as valuable in the first place. Albert Borgmann’s related idea of the device paradigm goes a step further: a device delivers a commodity – warmth, information, companionship – while concealing the practice that once had to be undertaken to earn it, so that we stop noticing the practice has gone at all. Only the commodity keeps arriving.

The costs of talking to a taxi driver are obvious: the small effort of politeness, the conversational roulette, the introvert’s tax of sustained small talk at 7am. The benefits are diffuse and deferred: the driver was, for many of us on many days, the last stranger we were obliged to encounter. The last person from outside our bubble – professionally, politically, socially – with whom we had an unchosen conversation. The last reliable source of a view we did not ask for.

Neither Susan nor I would design a world without those conversations. We simply enjoyed opting out of this one. And that, of course, is how such things are lost: never by decision, always by convenience. One comfortable ride at a time.

I should be clear that this is not an anti-Waymo article (the rides really were excellent, and I would take one again without hesitation – that is rather the point). It is an article about research. Because the same logic that removed the driver from the car is now, with equal serenity and considerably greater consequence, removing the collaborator from the research process.

The frictionless colleague

Large language models are the Waymo of intellectual life.

Consider the comparison honestly, as a researcher experiences it. A collaborator is available occasionally, between teaching commitments, grant deadlines and time zones; an LLM is available at 2am on a Sunday, which – let us not pretend otherwise – is when a worrying amount of research thinking actually happens. A collaborator arrives with their own agenda, their own framing of the problem, their own inconvenient conviction that your central assumption is wrong; an LLM arrives with no agenda beyond being useful to you. A collaborator must be persuaded; an LLM must merely be prompted. A collaborator will challenge you in ways you did not ask for and had not thought of; an LLM will challenge you precisely as robustly as you request – and not one degree more. If you ask it to critique your argument, it will do so, capably. But it will critique the argument you brought. It will not, unbidden, tell you that you are solving the wrong problem, that a rival group tried this in 2019 and abandoned it, or that your beautiful theoretical framing collapses on contact with the messy realities of someone else’s field.

The collaborator’s inconvenience, in other words, is not a bug in the collaboration; it largely is the collaboration. The value of another mind lies exactly in the ways it refuses to be an extension of your own.

And beyond challenge and serendipity lies something more fundamental still. Research is not merely a production function that converts ideas into papers. It is a community of practice – a social fabric maintained through argument, apprenticeship, conference-bar conversations, the shared ordeal of a difficult referee report. That fabric is woven from precisely the frictions we are now engineering away. Every conversation redirected from a colleague to a chatbot is a thread quietly withdrawn. No individual thread matters. The fabric does. There is a word for what happens when this proceeds at scale, and I propose we use it: decollaboration.

The incentive trap

If this were only a matter of individual temptation, it would be a subject for self-discipline and the occasional stern editorial. But it is not. The uncomfortable truth for research leaders, funders and institutions is that we have built an incentive system that makes decollaboration the rational choice – and we are busily making it more rational by the year.

Collaboration has always been expensive. It costs travel and scheduling and the glacial work of building trust. It costs ego management and compromise and the periodic small heartbreak of the author-order negotiation. Publish-or-perish has always taxed these expenses, because every hour spent aligning with a co-author is an hour not spent producing output that is legible to an evaluation system. But three forces are now compounding.

First, funding pressure. When budgets tighten, the first casualties are always the line items whose value is real but unmeasurable: travel, workshops, sabbaticals, visiting positions – the entire physical infrastructure of serendipity. We defund the corridor and then wonder where the corridor conversations went.

Second, velocity worship. Our evaluation systems – whatever DORA-shaped statements adorn our websites – still reward output and speed of output. And LLMs promise speed above all things. They whisper that the literature review can be done by Friday, that the draft can exist by Monday. To a researcher whose next position depends on the length of a publication list, this is not a whisper that is easy to ignore.

Third, and most seductively: the LLM never argues about author order. It has no ego to manage, no competing agenda, no rival claim on the credit. In a system where credit is the currency of survival, a brilliant interlocutor who demands no share of the credit is not merely convenient. It is arbitrage.

The research on research should give us pause here. We know from the team-science literature that small teams tend to disrupt while large teams develop, and that it is the unexpected combinations of collaborators – the atypical pairings across fields and institutions – that disproportionately produce the most novel work. Meanwhile, the early evidence on generative AI points in a direction that should sound familiar: individual productivity rises while the diversity of ideas narrows, everyone moving faster along increasingly similar paths. We are, in effect, running an uncontrolled global experiment in trading serendipity for throughput – and the incentive structures we have built are the experimental apparatus.

Writing is thinking

There is a deeper cost still, and it concerns the thing LLMs do most impressively: writing.

Part of the lure of these tools is the impression of speed. And it is worth being precise about why that impression is partly an illusion. Writing takes time because thinking takes time; the two are not separable activities that happen to occur in sequence. The value of writing a paper was never really the artefact – it was the forcing function. Writing is where we discover that the argument we were sure of has a hole in section three; where a vague intuition either becomes a precise claim or dissolves under the pressure of having to be one. High-quality ideas are slow to generate, and historically much of that slow generation happened between people: at the whiteboard, in the corridor, in the productive irritation of a co-author’s tracked changes.

Cognitive psychologists have a name for this kind of productive friction: Robert Bjork’s desirable difficulties – the finding that certain frictions in learning and thinking, effortful retrieval, delayed feedback, the struggle to get an idea onto the page in your own words, are not obstacles to durable understanding but the mechanism of it. Remove the difficulty for the sake of comfort, and you do not get the same understanding faster. You get less of it, delivered more smoothly.

Outsource the writing and you have not accelerated the thinking; you have skipped it. And the trap deepens as the models improve, because their output becomes steadily better – more fluent, more structured, less and less distinguishable from educated human prose. The temptation grows precisely as the tell-tale signs shrink. As a physicist, I am reminded of PT-symmetric quantum systems, which have the disconcerting property of behaving entirely normally right up until a hidden symmetry breaks – at which point everything changes at once. A research culture can look healthy by every visible measure – outputs up, turnaround down, prose immaculate – while the invisible thing that sustained it quietly falls below threshold. By the time the symptom appears in the metrics, the cause is years in the past.

Pilots and passengers

None of this is an argument for banning the tools. That would fail, and it would deserve to fail, because the promise is genuine. In a March 2026 Nature comment, Dashun Wang extends Steve Jobs’s famous description of the computer as “a bicycle for our minds”, proposing that AI agents are aeroplanes for the mind: faster and more powerful than the bicycle, harder to control, costlier when they crash. He is right about the upside, and it is considerable. When AI collapses the cost of failure, riskier and more ambitious questions become rational to ask; when it collapses the cost of analysis, small labs and lone newcomers can attempt what once required armies. Used well, these tools genuinely extend research – and, done properly, agentic systems could even strengthen one of science’s weakest joints, reproducibility, by making every analytical step loggable and replayable.

But notice what even the optimists insist upon. Wang’s prescription is what he calls pilot-in-command science: the researcher as captain, the agents as crew – an analyst to draft, a critic to probe, a planner to map next steps – with the human retaining authority over the question, the path and the conclusions. Even the most enthusiastic architects of AI-assisted discovery, in other words, are adamant that there must be someone in the front seat. And Wang arrives at the Waymo effect worry from the other direction: he warns that while AI may raise an individual’s performance, it risks reducing collective diversity, with outputs converging unless we deliberately counteract it – and that just as our collaborators shape us in profound and unexpected ways, so too will our agents. His remedy is to design for dissent and cultivate many models that think differently. Which is to say: having removed the friction, we must now engineer it back in.

This is an old worry wearing new clothes. In 1983, the psychologist Lisanne Bainbridge described the ironies of automation: the more reliably a system runs on its own, the less practice its human overseer gets at the very skill they are being kept in the loop to exercise in an emergency, so that competence quietly erodes precisely because it is needed only rarely, and always at the worst moment. Swap “pilot” for “researcher” and “autopilot” for “agent”, and the mechanism Wang is guarding against is the one Bainbridge named four decades earlier.

The problem, then, is not the technology. The problem is that we have made the human conversation the expensive option and the machine conversation free, and we are surprised at what researchers, responding rationally, then choose.

Fund the friction

So the responsibility falls where the incentives are made. Funders and institutions should recognise collaboration for what it is – not a nice-to-have, but a form of infrastructure – and price it accordingly.

Fund the friction: the workshops, the visits, the co-location, the unstructured time that produces the conversations nobody could have scheduled. Evaluate contribution rather than velocity, and mean it. Treat “who did you think with?” as a question worthy of the same seriousness as “what did you publish?”

And perhaps, in an age when a machine can produce any number of competent papers, we should notice that the scarce and valuable thing has inverted: the output is becoming cheap, and it is the thinking together that is becoming precious.

The return journey

Susan and I got where we were going. The car was smooth, the silence was comfortable, and the collaboration for which we had each crossed an ocean and a continent happened – not in the frictionless capsule, but in the inefficient, unscheduled, thoroughly human hours that followed it: the conversations that wandered, the disagreements neither of us had planned, the ideas that neither of us brought to San Francisco because they did not exist until we were in the same room.

That, in the end, is the Waymo effect in full: the ride is delightful, the destination is reached, and it is only if you glance up front that you notice what is missing. Wang calls for pilot-in-command science; the quiet risk is that our incentives are training us for passenger-in-comfort science instead.

Research is now climbing into the back seat. The ride will be smooth. The output will arrive. The question that research leaders should be asking – before we have all had a few more years of comfortable rides – is a simple one: who is in the front seat? And when did we stop noticing that nobody was driving?

Daniel Hook is Chief Scientific Officer at Holtzbrinck Group. With thanks to Susan Winslow and Oliver Zahn for interesting conversations that motivated this writing.