Tag Archives: press

The Weird Incentives of Word Count

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Many freelancers get paid per hour, or per day. Some have standard rates for typical tasks.

Journalism is rarely typical. And usually, we’re paid per word.

I’ve had many friends whose first thought is how easy that must be to exploit. Often they remember a story about a writer like Charles Dickens or Victor Hugo padding their books with digressions to up their pay. Those stories probably didn’t happen, and these days, that method is even less likely to work. When I pitch a piece, the editor and I agree beforehand on a target word count. The only way I get paid more than expected is if the editor agrees that it makes sense.

That said, there definitely are ways to exploit being paid by the word, if you wanted to. They’re just more subtle.

The source of any such exploit is to notice that writing is not actually the hard part of journalism. Research is time-consuming: the more people you talk to, the longer it takes. Writing, once you’re used to it, can go quite quickly. So the length of a piece itself doesn’t determine how much work it takes to write: research does.

In principle, longer pieces are supposed to take more research. Editors will have rough expectations for the number of sources you’ll interview, so this isn’t something you can skimp on, per se. And publications known for their high standards for research usually pay larger rates per-word.

But no matter how much you’re paid, research can go out of hand in the other direction. Journalism is about finding out the truth, and sometimes the truth is more complicated than expected. Sometimes you need more information, and need to fit in one more interview, then another, or run around chasing after missing info. Then you need to figure out how to fit it all into the same word count you started with.

So suppose you’re a freelancer who aspires to consistently pay their bills. You want to earn a certain amount per hour worked, but some projects can take much more work than others. So what do you do?

You look for the simple. You try to find stories that are unlikely to be complicated, that won’t have clashes of opinion that need to be represented or experts with different sides of the story. If you don’t find such a story, you simplify: you write a piece that makes the story sound cohesive, that papers over doubt, even if you’ve run into doubts yourself while writing it. If you’re consistent about it, you can do what your friends with salaries do, and earn a stable living.

Not the kind of exploit you had in mind, I suspect. And not, despite the sound of it, something you can do reliably. I find I’m always tempted by complexity, always drawn to investigate a story that’s a bit too big for the wordcount it’s supposed to fit.

But it is an ever-present incentive, one I suspect people with a larger roster of leads start to feel. If you’re paid by the word, then the best story tends to be a tidy one.

Showing Me Your Amateur Breakthrough Is Evidence That It’s Wrong

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Ok, people who read my post on the Anthropic science blog: we need to talk.

It’s understandable. You saw me write about AI being used to tackle a serious, publishable physics problem. Naturally, some of you want to share that you accomplished something even more impressive with AI, something the scientific journals and arXiv moderators won’t acknowledge. Maybe you’re hoping I’ll write about it, maybe you want me to check your math. Maybe you just think it’s neat.

But here’s the thing: most of the time, I’m not the right person to send those ideas to. And the fact that you didn’t figure that out is a bad sign.

Outside of the movies, scientists are specialists. Not just “physics” or “particle physics” or “theoretical particle physics”, but so specific that they often focus on particular methods, or are geared around particular goals. For any idea you propose, if it’s good, there will be a small list of people who will be excited to see it and able to evaluate it. They’ll be people who are trying to accomplish the same thing, or people who used the methods you’re using for other things. Those people aren’t hard to find, if your idea is in a field you’re actually interested in: they’re the people who write papers on the subject. If you find the topic interesting, you’ll have read their papers, and know who to reach out to.

So even back when I was active as a physicist, there was only a small list of ideas where I’d be the right person to talk to. Now, though, as I mentioned in the Anthropic post, I’m not a scientist anymore. I haven’t been following the field in enough detail to actually verify anything in detail, even on topics I used to research. Part of the punchline of the post was that I don’t have a clear idea of what’s possible in the field right now. So even if you’ve figured out something in scattering amplitudes, you should reach out to someone who’s still researching the subject, a few of whom were even mentioned in the post!

Now, it might be you’re not interested in someone checking your work, and instead you’re writing to me in my capacity as a science journalist, because you feel like you did something newsworthy. But as a science journalist, I’m not supposed to evaluate your work by myself. I’m supposed to reach out to an independent expert, and if I can’t find any who will speak positively about your result, then the editors won’t publish it. So if you think you did something big enough to merit news coverage, the first thing to do is once again, reach out to a scientist who actually has the expertise to evaluate your work, and get them excited about it. Then you two can contact me together!

If you don’t know who to contact, that’s a sign you don’t read papers in the field. It makes it more likely you managed to convince yourself of something nonsensical, because you don’t have the expertise to evaluate it. It’s a pretty clear sign that I shouldn’t trust you or your AI methods about the result.

It’s also just plain bad for you. If you produce a paper without ever wanting to read any, it suggests your priorities are in a dangerous place: that you’re trying to do something that sounds cool and important over doing something you actually care about.

I’m coming from personal experience here.

As an academic, I spent years telling myself that not enjoying reading papers or going to talks was just part of the job. Then it really cost me something, and I realized the job wasn’t worth my superficial desire to be a physicist. So I left.

Then, I spent a year trying to get a job in industry, and was told the same thing. They wanted to hire people who cared about the job. The finance guys wanted to hire people who read books about finance. The data scientists wanted to hire people who did projects in data science. They wanted, if not passion, then at least grace. Someone who could take to the job easily, and naturally.

Now, what I do for a living is something I was doing once a week, for no more incentive than a vague sense of personal obligation, for over a decade. And I’m doing a lot better.

You want to do something impressive with AI? You think you’re a whiz with Claude Code, that your custom workflow is immune to AI hallucinations?

Then use it for something else. Pick something you actually enjoy reading about, not just the pop news, but the real technical literature. Something you don’t need the AI to explain to you. Something that matters to you, in a way that actually counts.

At the very least, it’ll work out better than writing to me.

Everybody Who Isn’t ”Viewers Like You”

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Last week, I talked about how truthseekers get paid. But truth-tellers and truth-seekers are different things.

Consider educational kids’ shows on public television.

Nobody who works on Sesame Street is out there uncovering new letters and numbers. Bill Nye’s show wasn’t bringing analysis fresh from the lab.

The purpose of these shows is to educate. The purpose of education is to change minds.

So who pays for educational kids’ shows on public television?

If you’re from the US and watched PBS growing up, you remember one answer: “viewers like you!” US public television is supported by donations, ordinary people across the country who want it to keep on educating kids.

But you also might remember the lists of names that came before “viewers like you”. Some of those were things like “the Department of Education” or “a grant from the National Science Foundation”: government programs, in other words. Others were philanthropists and private foundations. Some were tied to companies, like the Intel Foundation, or Juicy Juice.

All of these groups, from government departments to donors, are trying to change kids’ minds. They support specific shows on specific topics, where they want kids to be better-informed. The same groups have the same kind of impact on schools. For example, I remember in elementary school we all learned to play a recorder, because a wealthy donor had given the school recorders out of the idea that music education was especially important.

For a truth-seeker like a journalist, accepting that kind of funding would be a problem. Grants for journalists tend to support things like travel, letting journalists learn more about specific topics, not pre-judging the conclusion. But children’s television is about truth-telling, not truth-seeking, so our standards are different. We trust the people making children’s television to care about whether they’re telling the truth. And because the topics aren’t new, we don’t usually worry about their judgement being biased.

All this is rather obvious. But now, consider science YouTube.

Some science YouTubers seem to have a mission much like children’s television. They’re there to teach, not to make independent judgements. They don’t search for truth on their own. And some of them are funded by educational grants, much like children’s television.

Others are a bit more like journalists, or even activists. People follow them for their opinions, to hear their assessment. They’re trying to be truth-seekers.

On YouTube, it’s not always obvious which is which.

There’s a particular group of philanthropists called Effective Altruists, and many of them are concerned about AI. So in between funding things like anti-malaria bed nets, some of them are giving grants to YouTubers to make educational content about AI-related risks.

Apparently, they reached out to Sabine Hossenfelder, which was a bad idea. Sabine Hossenfelder’s followers aren’t just looking for education on known facts. They’re looking for her judgements, her literal bullshit-rating on ideas. And so while she’s paid by “viewers like you”, she’s not really the type to get paid by that type of grant.

What I want to emphasize, and what looked like it was getting lost in the discussion, was that their pitch would have been totally reasonable for other YouTubers. Educators do occasionally get grants to educate on specific topics. This is in fact a totally normal thing. Some YouTubers are educators first and foremost, they aren’t there as truth-seekers, but truth-tellers, with a real difference in how careful they need to be about bias.

Some YouTubers are different from other YouTubers. News at 11.

Paying the Truthseekers

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Academics and journalists have a lot in common, at least in principle.

Whether you’re a reporter or a professor, your job is to go out into the world and figure out the truth. You’re supposed to be careful, to check and correct for how you might be wrong. And at the end of the day you’re supposed to communicate what you found.

The differences mostly come in how you’re paid.

You could imagine some sort of pure truthseeker, paid purely by how well they tell the truth. People would ask them to find out the truth about something, and pay them for the service. And the truthseekers with the best track record would get the most clients. But neither profession really works like this.

Journalism comes closest. Once upon a time, people bought newspapers in order to be the first to know when something important happened. While there’s still a little bit of that going on (I guess this is what Bloomberg Terminals are for?), it’s a lot less central because of the internet. Now, there are hundreds of ways to find out about things, from a multitude of news sites to social media. More and more, people expect to be able to get information for free.

In that environment, the news has to compete not on the facts themselves, but on how it presents them. People pay for news that’s curated well to match their interests, or news that feels more respectable. And more than either of those, they pay for news that’s entertaining. So while truthseeking skills pay, writing skills often end up mattering more. In a sense, it’s why it’s possible for me to do journalism at all. I was trained in the academic truthseeking tradition, not the journalistic one. I got into journalism by impressing editors with my writing, not my ability to suss out the truth.

That academic truthseeking tradition is quite different, in part because the rewards for it are much more indirect. Academics pay comes from two main sources: research grants, and student tuition. Students are mostly there to learn old facts, not new ones, so that source of money supports research only in so far that students believe that a successful researcher with time for research will also be a better teacher.

Research grants, in principle, pay for truthseeking. But they’re typically paid by governments, which often don’t have a clear idea of what they’d like to learn, since the more practical questions are already being researched by private companies. So the decision gets delegated out to other academics, who have a vague shared sense of what’s worth knowing and what’s not. Accuracy should have an impact: that is, it should be easier to get grants if you’re better at finding the truth. But in practice, unless someone does so badly they trigger a scandal, academics don’t usually get things all that wrong. So grants are mostly based on other factors.

Paying someone purely to deliver the truth, not to entertain or match a culture, seems tricky. You could imagine sci-fi scenarios. What if we could track the logic people used to make decisions, and demand payment if those decisions were based on facts we uncovered, like a journalist getting a percentage of every short made in response to bad news they dug up about a company? What if governments paid in proportion to how valuable academic ideas turned out to be, centuries after they were discovered, and modern-day academics sold shares in that future payout to fund themselves? What if prediction markets something something?

For the moment, academics and journalists are both in a weird middle space. They’re truthseekers, still, by culture and inclination and desire. But they’re paid for something else.

Newsworthiness Guide for Scientists

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I had a recurring “elevator pitch” at Lancefest earlier this summer. After explaining that I’m a science journalist now, I’d end with “so if you run into a story, let me know!”

One person had a question that left me stumped: “What counts as a story?”

For those of us who don’t happen to be Einstein

I didn’t have a good response then. I’ve got a better one now, though I’m afraid it doesn’t fit in an elevator pitch. This is all based on my experience, so take it with a grain of salt. But here are the criteria that seem to matter:

First, a story usually needs a news hook. News is, in particular, supposed to be “new”. That doesn’t mean I can’t write about history, or established science. But editors like those stories a lot better if there is some recent development, within the past year or so, to tie it to. The new development doesn’t have to be all that important, the story can mostly focus on something else. But it needs to be somewhere in there.

Second, news stories are usually qualitative, not quantitative. I need to be able to tell a story about what happened, what actions people took and why they mattered. Quantitative developments usually only make the news if they’re so big that they shade into the qualitative: something doubling unexpectedly, for example.

Third, ideally a science news story is something that is getting the experts excited. Journalists aren’t supposed to judge the scientific merit of ideas on their own, they’re supposed to rely on experts. The most solid stories, the ones that are easiest to pitch, are ones where there’s a community of experts that largely think something is cool. That makes it easier to get good quotes, and easier to justify its relevance. If you accomplished something and you’re having trouble convincing anyone it matters, don’t start with me, start with your colleagues!

Fourth: less importantly, it helps when stories have a human angle. If you can tell a tale about how you came up with an idea, if you came from an unusual background, if something was hotly debated but now is deemed essential: these things sweeten a story, they capture readers’ interest, and editors see their value.

Finally, stories involve something changing. It can be something that just changed now, for a news piece, but it can also be something that changed over time, for a feature in a magazine. The key is change. “Old method still works” is not going to excite people, and it won’t count as news.

After writing all that out, I’m still not sure I answered the original question. But hopefully I’ve at least given some tips that can get you started. If you’re a scientist, and you see something that hits most of the boxes on this list but hasn’t been covered in the news yet, consider reaching out to me. You may have run into a story!

Bonus Info on Dark Energy and Muons

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I had two pieces up this month, one in New Scientist and one in Quanta. I figured I’d give a bit of “bonus info” for both here.

The New Scientist piece covered a paper arguing that, according to the supernova evidence, there is no need for dark energy, because the universe isn’t speeding up after all. That’s a pretty dramatic claim, and it’s one the majority of cosmologists don’t agree with. But a small group has been hammering at the consensus.

Long-time followers of this blog have already heard of these folks. I criticized news coverage around a paper with one of the authors, Subir Sarkar, back in 2016, when he was only arguing that the supernova evidence was insufficient to establish dark energy, not that it was literally the wrong way around. He and his co-authors wanted an opportunity to state their case, so I posted their response a few weeks later. Later, I got to know Rameez, another author on the new paper. He ended up doing a guest post in 2019, on the next step in the story.

That story went from a statistical critique to an active proposal for something outside the consensus, in the form of inhomogeneous cosmology, the idea that the universe may be lumpier than typically assumed, with flowing currents that explain things otherwise attributed to exotic physics. That’s another topic I’ve covered before, but in this article I didn’t have the space to go into it.

Sarkar and Rameez are claiming something much more radical than most proponents of inhomogeneous cosmology, arguing against dark energy as a whole, not just more subtle effects. They told me a lot about their reasoning, most of which also didn’t make it into the piece.

It’s largely not going to make it here, either. Not due to space limitations: I’ve been pushing the blog post lengths you folks tolerate for a long time, no reason to stop now. Rather, it’s because I genuinely don’t feel qualified to judge this.

The issue is, cosmology is messy.

How do you figure out how the universe is expanding? You can look at supernovae, and use how dim they are to estimate how far away they are. The issue is, supernovae vary, even famous “standard candles”: they move in different ways, they shine differently depending on different things. Simulations aren’t nearly advanced enough to tell you all this from first principles, and so there are a wash of different corrections based largely on empirical observations, taking this or that correlation seriously and factoring them out of the measurement, while disregarding other correlations as spurious. Which of these combinations of corrections you trust determines whether the universe seems to be accelerating or decelerating.

It makes me glad I went into particle physics instead of cosmology, where you can mostly model everything from first-principles, and compare to your models.

Of course, even that can get you into trouble.

Take that Quanta piece. I wrote an update of the muon g-2 measurement. In some ways, this is an issue that many think of as already solved, by precisely that kind of first-principles modeling. Muons seemed to be violating the Standard Model, according to a calculation based in part on empirical formulas. Lattice QCD researchers figured out how to do that part of the prediction via a first-principles simulation instead, with enough accuracy that it could substitute for the empirical formulas. And lo and behold, the new prediction agreed with the experiment. Eventually, the experts accepted it, and all was well.

Except, as I commented at the time, not really. Because the empirical formulas were based on other experiments, colliding electrons and positrons. And as I now understand in much more detail, the disagreement between those electron-positron experiments is worryingly large.

So nobody is resting on their laurels. The lattice folks improved their calculation, with a result published in Nature this year that prompted Quanta to ask me to report on it. The people using the empirical method are still trying to sort out what happened. And the experimentalists are busy scrutinizing how they analyze their data, and collecting and analyzing more.

A few details that didn’t make it into the piece:

First: it goes beyond the level I was aiming for in the piece, but I really do want to emphasize the role of error estimates. Ultimately, every step in the story was not just one where experiments and predictions disagree, but one where they disagree past their reported error. The problem with disagreement between the new experiments isn’t that they disagree, it’s that they disagree so badly that they’re straining the statistical methods the people using the empirical method use to combine results together, so badly that if taken seriously, those methods would have to throw away ten years of progress of increasing precision. I don’t expect it, but I really hope for a postmortem in which we learn how to better estimate the kinds of experimental errors that can cause this. I haven’t seen that kind of postmortem for other results, so I don’t expect one here. But I have to believe that in the background someone is learning something, and getting better at this.

Second: one interesting question my editors raised was whether it was possible to do a first-principles calculation to compare with the electron-positron experiments. The answer is yes, but with some caveats. One will be recognizable to physicists: lattice QCD can only compute energy-integrated cross-sections, not measurements at particular energies like experiments find. But they can compute integrals with a modulating function…such as one strongly peaked at a particular energy. It’s a familiar type of trick for a quantum field theorist, though in this case it’s trickier than it sounds. I actually misunderstood, and thought that the same people I was talking to about the muon g-2 calculation had done that, and found it agreed with one of the electron-positron experiments over another. That’s not true: their comparisons were more indirect, while other groups attempting more direct comparisons haven’t quite gotten something good enough to do more than gesture at an agreement. So while the direction was correct, there are indeed lattice-based suggestions that favor one experiment over the others, the piece phrased things much too definitely. There’ll be a correction fixing this.

Bonus Info for “100-year-old assumption about the universe may soon be overturned”

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I had a piece up in New Scientist last week (paywalled, sorry!), about a new analysis that suggests the universe is less homogeneous (more “lumpy”) that most cosmologists believe.

The piece was a bit different than my usual. Normally I do what people in the biz call “features”: longer articles about general trends. This was a much more classic “news piece”. The people I interviewed had several papers up in early April, the editors at New Scientist thought they were interesting enough to write about, so I was asked for a short, timely piece with the key takeaways.

That means I didn’t have a ton of space for background info. So if you’d like to know more, this post is for you!

The 100-year old assumption in the title refers to the Friedmann–Lemaître–Robertson–Walker (or FLRW) universe, an idea that first came together in the 1920’s, where cosmologists model the universe as homogeneous and isotropic: the same no matter where, or in which direction, you look. That sounds like a crazy assumption, but on the largest scales we can measure it’s actually mostly fine. Once you’re trying to calculate ripples in the cosmic microwave background or find out how fast distant galaxies are accelerating away, it works surprisingly well to act like the universe is an evenly-mixed soup of matter, radiation, dark matter, and dark energy.

But every assumption in physics has its doubters. The doubters of homogeneity are known as inhomogeneous cosmologists, and I’ve been sympathetic to their complaints for a while now.

I even let an inhomogeneous cosmologist do a guest post on my blog, back in 2019. That post argued something dramatic: that dark energy may not even exist, but that measurements of accelerating expansion may be a consequence of a dramatic lopsidedness in the universe around us.

The people I covered in New Scientist, Asta Heinesen, Tim Clifton, and Sofie Marie Koksbang, are arguing something much less dramatic…but that’s part of what makes it more compelling. Instead of arguing that the universe is dramatically uneven or lopsided, they’re arguing that the universe can still be on average smooth and homogeneous, the soup of galaxies people seem to expect…but still, can’t be fully modeled that way.

This is a tricky distinction to explain, and certainly something I didn’t have space to cover well enough in New Scientist. But let me take a stab at it here:

Any cosmologist will agree that FLRW can’t be the whole story. We know the universe isn’t a perfectly mixed soup: there are galaxies, and stars, and black holes, and they all wiggle the fabric of the universe in different places. When they study the universe as a whole, they’re averaging out all of that, to get the overall behavior, a bit like you could average the number of children in each family to get the average children per family in a country.

But FLRW isn’t just an average, it’s a model of spacetime. Because of that, it has to obey certain equations, called Einstein’s equations. It has to make sense by itself, as the correct answer for how spacetime would behave if it were filled with a uniform soup.

That’s an extra restriction, and that extra restriction can get you in trouble. To continue with the analogy, any real family has a whole number of children. But the average family doesn’t have a whole number of children. When I was born, the average family in the US had around 2.5 children. A lot of cartoons imagined what the half-child looked like.

From the perspective of Heinesen, Clifton, and Koksbang, assuming FLRW is a bit like assuming that the average family must have two children, or three, and can’t possibly have 2.5. Averages don’t have to look like sensible spacetimes, they don’t have to obey the Einstein equations.

In practice, the assumption of FLRW has worked a lot better than assuming that the average family can’t have 2.5 children, and that’s why Heinesen, Clifton, and Koksbang are cautious. They’re not claiming that inhomogeneity can explain everything, all the way to major components of the universe like dark energy. But they do think it can be a good explanation for smaller effects. And as cosmologists worry about smaller and smaller effects, wondering if dark energy changes over time and why the expansion rate of the universe doesn’t match up between different measurements, it can be important to remember that averages aren’t all-powerful. Eventually, they can break down. It’s a more subtle issue than a fractional child. But, as I covered in New Scientist, it may already be happening.

Breakthrough Prize 2026

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Because of last week’s “bonus info” post, I’m only now getting around to commenting on this year’s Breakthrough Prizes in Fundamental Physics. While I don’t comment on them every year, I know enough about several of this year’s winners that I figured a post would be helpful.

For those who haven’t heard of it, the Breakthrough Prizes are a bit like the Nobel, if it was created by a 21st century rich person instead of a 19th century one. They give out more money, and instead of an organization like the Swedish Academy of Sciences they pick winners via a committee of past winners. They’re more flexible in structure than the Nobel, with extra prizes for early-career researchers and a tendency to reward accomplishments that are either entirely theoretical or solid experimental work that doesn’t show a new discovery, both of which are things the Nobel Prize is structured to avoid. They’ve also shown willingness to reward large collaborations, rather than following the Nobel’s informal rule to only give the award to three people at a time.

This last was on display this year in their main award in physics this year, for the muon g-2 collaborations. The award is going to collaborations of scientists and engineers at three different particle colliders, for work done over a span of over fifty years to measure the magnetic properties of the muon. These measurements have shown a tantalizing discrepancy with predictions that inspired many to conjecture new physics. However, in the last few years it’s looked more and more like the discrepancy was due to an imprecise prediction, and better methods seem to be converging to the experimental value. At this point, smart money is that there is no disagreement with the Standard Model here, but as always in science there’s a chance some mystery remains.

The Breakthrough Prize also offered a special, out-of-schedule prize to David Gross. Already a Nobel laureate, Gross had a crucial role in our understanding of the force of quantum chromodynamics that binds protons and neutrons together. He was also a major founding figure in string theory, and since the Breakthrough Prize is more comfortable recognizing theoretical contributions they get to mention this as well. Gross is also known in the community for his personality, which tends to fill up any room he’s in. I can only imagine the conversations that led to Breakthrough’s decision to add a special prize for him this year.

Breakthrough is also adding a new recurring prize, the Vera Rubin New Frontiers Prize, honoring women who make important contributions to physics within two years of their PhD. The prize is a bit smaller than the exiting early-career New Horizons in Physics Prizes, presumably because it goes to even younger researchers. This year’s winner is from my old field, scattering amplitudes. Carolina Figueiredo is part of the latest evolution of the research program behind the amplituhedron. The new framework of “surfaceology” seems like a promising geometry-flavored way to understand particle physics calculations in more realistic theories, and unlike its predecessors may have some practical value eventually as well. Congrats Carolina!

Finally, the New Horizons in Physics Prizes are for impressive early-career researchers. I don’t know much about the first recipient, Benjamin Safdi, who works on searches for axions and axion-like particles, today’s most trendy dark matter candidate. I know a bit more about the work done by Clay Córdova, Thomas Dumitrescu, Shu-Heng Shao, and Yifan Wang, having met several of them in my physics career. They work on what are called generalized symmetries, concepts which go beyond the usual idea of how symmetry is supposed to work by involving more complicated tensors. I saw these crop up a fair bit in talks, but they were distant enough from my area that I never had a particularly clear grasp of what people were doing with them. I know even less about the work of the last three, Dillon Brout, J. Colin Hill, Mathew Madhavacheril, Maria Vincenzi, Daniel Scolnic, and W. L. Kimmy Wu, on cosmological measurements, but I was friends with Mathew in grad school and am impressed that he’s now working on cosmology given how little cosmology research there was at Stony Brook at the time.

Bonus Info for “Quantum ‘Jamming’ Explores the Truly Fundamental Principles of Nature”

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I had a new piece in Quanta Magazine last week, about a hypothetical trick in theories beyond quantum mechanics called jamming.

Sometimes, I get science news stories from contacts. Sometimes I see an academic post something cool on X or Bluesky. But when the stories aren’t coming easy, I open up arXiv.org, click on “new”, and start browsing. And occasionally, I spot something cool.

That happened with jamming. I saw the concept mentioned in an abstract, the idea that someone could “jam” quantum entanglement from afar, like you would jam a radio signal. I hadn’t heard of it before. I wanted to know more. And after I talked to Quanta’s editors, they wanted to know more too.

Jamming is not possible under the rules of quantum mechanics we know. Instead, it’s something that could be possible in a kind of super-quantum mechanics, a theory even weirder than the famously weird theory we use today. In my piece for Quanta, I talked about where the idea of jamming comes from, and why it’s spurring discussion in recent years. In this post, I wanted to give some “bonus info” that didn’t fit into the piece.

One theme I didn’t have as much space to explore is causality.

Quantum mechanics famously seems to do weird things with cause and effect. In a double-slit experiment, photons pass one by one through one of two slits in a wall, headed to a photographic screen. No matter how slowly and carefully you send the photons, their distribution on the other end will show interference between the two possible paths, one through each slit, even though each photon only goes through one. It’s as if before hitting the screen, the photons are simultaneously traveling on every possible path, only to pick one in the moment the photon is detected.

Einstein was bothered by this. He imagined a photographic screen so large it would take light years to cross. How could detecting a photon on one side change the possibility of detecting a photon on the other side? That seemed, to him, to require signals traveling faster than light, which in turn would screw up cause and effect, as any way to send a signal faster than light can also, from another perspective, send a signal back in time.

The answer most physicists accept is that no signal can be sent in this way…at least, in the modern sense. Quantum outcomes are random, so while you could imagine that a measurement in one place changes the outcome in another place, your choice to measure has no effect on that distant outcome. You can’t intentionally send a message faster than light. We call that “no-signaling”, and it prevents the paradoxes of time travel.

Jamming obeys similar rules. A jammer (in the story in my article, a magician named Jim) can modify the entanglement between two distant particles, seemingly faster than light. But he can only do this in a way that involves randomness, so that the probabilities for measurement results for each individual particle stay the same. Instead, he can only modify how measurements between the two particles are related, their correlation. And he can only do this if the two particles can only be compared in a region that he can reach without traveling faster than light.

That’s enough to allow Jim to break the security of many quantum cryptography procedures. He can do this for example by mimicking entanglement: quantum cryptography often uses entanglement to verify that a message hasn’t been tampered with. If you can modify correlations from afar, you can make two particles appear to be entangled when actually they’re related by some other rules, which give you access to the secret that others are trying to hide.

Part of what’s still under discussion, is whether that kind of trick is compatible with causality. This depends a lot on how you think causality is supposed to work, and while the people I talked to are trying to get the story straight, they weren’t in agreement yet. In particular, Vilasini and Colbeck seemed to think that there was an important difference between the way that jamming bends causality and the way that ordinary quantum mechanics does, while Eckstein and Ramanathan weren’t so sure.

More broadly, Vilasini and Colbeck have a broader way of thinking about causality that I only barely touched on. Part of that is ways you can think of one event causing another even if no signal can be sent between them. Part of that is time loops, but of a limited kind: loops that can’t cause paradoxes, because they’re loops of causes, but not intentional signals. Vilasini and Colbeck have argued that jamming, if it existed, could be used to set up these kind of limited time loops, in a piece that was covered by New Scientist. It should be emphasized that these are really very limited time loops, for more reasons than one. They’re also limited to being in only one spatial dimension: that is, everyone in the loop has to be lined up in exactly a straight line. And I got the impression they also require everyone to activate their measurement or jamming devices instantly: with any small delay, the loop breaks.

I said even less about Mirjam Weilenmann’s critique, because there were bigger aspects that the researchers still disagreed on when I spoke with them. Weilenmann’s argument looks at what happens when there are multiple jammers, jamming different pairs of entangled particles. I got the impression from her that she felt she had found a contradiction in these examples, where jamming could only work if it broke its essential no-signaling rules. But Eckstein and Ramanathan seemed to think she was describing a scenario where one jammer could cause noise that would disrupt another jammer, “jamming the jammers” in a sense that didn’t cause any fundamental problems, just introduced jammer vs. jammer combat to make the story more interesting. I opted to not say much about this, since it was clear that things weren’t resolved yet. The researchers are still talking, and I look forward to hearing what they conclude when they reach agreement.

I also didn’t say much about tests in the real world. But that is something Eckstein and collaborators are actively exploring. They’re investigating experiments that could show deviations from quantum mechanics in a variety of contexts, from tabletops in university labs to particle colliders. The hope is that some of these strange ideas could actually be tested.

In general, the impression I got was that despite the seeds of this topic being laid thirty years ago, and reintroduced to the field ten years ago…the topic is heating up right now, in a way it hadn’t before. I’m expecting more jamming papers. If they’re cool enough, I may even cover some of them.

Trust Is a Tree

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Scientists trust what they think they can verify.

In principle, you can work your way through the proof of every mathematical theorem. With enough money and time, you could replicate every experiment. For every expert opinion, you could dig through the literature and find how it was justified.

And while a scientist can’t actually do that for every field, they might be able to for the ones they care about most. In your specialty, you probably can check the logic behind every claim. And you know that enough people try, that you can trust your colleagues’ work.

As a science journalist, most of the time, you can’t do those checks. You don’t even pretend you can. Instead, you build trust, like a tree.

You start with a grounding. A former scientist might trust their former colleagues, people they trusted, as a scientist, to do (and know) good work. A non-scientist has to start somewhere else. They might use prestige, looking up those tenured folks at Harvard or Princeton or Stanford. They might look to who other journalists trusted, scientists who’ve already been in the news. They might track journals or roles, assuming that a publication in Nature, or a position on a national grant committee, has a special meaning.

And if things stopped there, it would be a pretty elitist system. It still can be, and often is. But there is another step, which softens it.

The trust builds.

When I want to know if a paper in an unfamiliar field makes sense, if it’s worth covering, I try to ask someone I trust. Sometimes, they don’t know, and shrug. Other, more useful, times, they don’t know, but they have a suggestion: someone they trust, who can give me the answer.

And so I ask the new person, and now I trust someone more.

And suppose the new person says the new paper is good, and worth covering, good science and all that jazz.

Well, now I can trust its authors too, right?

So when the next paper comes, I now don’t just have that first someone. I have the person they recommended, and the authors of the previous paper.

The trust builds out, and up, like branches on a tree.