She could’ve been Erdős-1, but she was shy

Several years ago I was at a network science conference in Tel Aviv, organized by Albert-László Barabási and Baruch Barzel. After the talks a few of us walked to a pub next door. It was full. A woman asked if she could take the empty chair at our table, then asked what we did. Network science, we said. She smiled. “I know a little about that. At the end of my PhD, Paul Erdős offered to write a paper with me. I was too shy, so I said no.”

If you are not a mathematician: Erdős was one of the most prolific mathematicians who ever lived, and the field measures closeness to him by how many co-authorship steps separate you from him, so writing a paper with him directly gives you an Erdős number of 1, a small and lifelong badge of honor. She could have had it. Even before earning her PhD!!! She was too shy to say yes.

She told it lightly, with a smile, decades later. That is the part that stayed with me. Nothing too serious. Just a door she did not walk through, and a life that quietly closed around the decision. She was, I would guess, barely 60 that night. Back then that looked old to me. I am now not far from it myself.

Why am I telling you this?

People are shy about their own work, and many of us were raised to treat self-promotion as something a little shameful. This is not spread evenly. Women self-promote markedly less than equally-performing men, a gap that shows up as early as sixth grade and persists even when there is nothing to gain by holding back (Exley and Kessler, “The Gender Gap in Self-Promotion,” Quarterly Journal of Economics, 2022). And when women do self-promote, they are often penalized for it, judged less likeable and less hireable (Rudman, Journal of Personality and Social Psychology, 1998). So the reluctance is not a character flaw. It is a rational response to a real bind.

But shy people, men and women alike, shortchange themselves and the rest of us. If you do good work, it is your job to make it visible. A good job nobody can find is not really a good job. Unless you are a deep-cover spy, in which case, carry on.

So what do you do about it?

First, reframe it. You are not bragging, you are leaving a trail. “Here is what I did and where to find it” is documentation, not a peacock display, and that framing also sidesteps most of the backlash, because it points at the work and not at you.

Second, tell the few people who would actually care, directly. You do not have to shout into the void. A short note, with no ask in it, to the handful of people who would genuinely want to know is real visibility, and it almost never feels like self-promotion.

Third, make it a habit, not a performance. A small, regular trickle of “here is what I learned this week” beats one agonized announcement a year, and it never requires you to work up the nerve for a big reveal.

And if a weekly visibility habit is exactly the kind of thing you will quietly let slide, automate it. That is the bet behind Loud Camel, a tool that helps researchers get cited and recognized: it runs the visibility steps on a schedule, so good work gets surfaced even in the weeks you do not feel like showing up.

The shy person’s favorite excuse is “I have nothing worth sharing right now,” and a blank screen is happy to agree. So this week I changed how Loud Camel handles that moment. It now always proposes at least one thing to publish, even when nothing obvious is in the queue, and more when good openings are scarce. It varies the angle each time, so even a saturated account keeps getting fresh suggestions instead of repeats or an empty page. You still have to do the un-shy part and hit publish. Loud Camel just makes sure there is always something there to publish.

She did excellent work for decades. She just never let most people see that part of it. מי שמתבייש מתייבש, the saying goes: the shy one dries up. Do the good work. Then make sure someone can find it.

PS. I never asked her name. The pub was loud, the night wound down, and I was too shy to ask a stranger for her email. I still think about it. She had spent a whole career in the same field Loud Camel works in, and I could have asked her to look at what I am building. I did not. So this is a post I had to write to myself too.

It’s not the Matthew effect. It’s the Daniel effect.

When I worked at Automattic, the company behind WordPress.com, one of the things my team looked into was what makes a blog post get likes. We had data showing that people who don’t get likes early tend to quit blogging. The likes aren’t vanity. They’re the fuel that keeps someone writing.

Why does early success predict later success?

So we went looking for the best predictor of whether a post would get likes. We checked the obvious candidates: topic, length, time of day, whether it had an image. The strongest predictor, by a wide margin, turned out to be embarrassingly circular. It was whether the author’s previous posts got likes.

That’s it. The best way to get likes on your tenth post is to have gotten them on your ninth. It’s a chicken-and-egg trap, and it’s a little sad. The people who most need the encouragement, the ones starting from zero, are exactly the ones least likely to get it.

Blogging isn’t special here. Authors who made money on their last book are the ones most likely to make money on the next. The same circular pattern shows up almost everywhere you look for it.

Sociologists have a name for this. In 1968 Robert Merton called it the Matthew effect, after a line in the Gospel of Matthew: “to everyone who has, more will be given, but from the one who has not, even what he has will be taken away.” Merton chose that verse precisely because it sounds unjust. He was describing how famous scientists collect the credit for work that less-famous scientists did just as much of. Recognition accrues to whoever already has it. (Robert Merton, “The Matthew Effect in Science,” Science, 1968.)

Will AI finally level the field for newcomers?

For most of history this trap looked permanent. You needed an audience to get an audience, a track record to earn the next one, capital to attract capital.

And then AI arrived and looked, for a moment, like the thing that finally breaks it. Suddenly anyone can produce a clean essay, a working script, a competent analysis. The surface of expertise, the polished output that used to take years to fake, now costs twenty dollars a month. If the Matthew effect ran on access to knowledge, AI should be the great leveler.

Here’s the claim I want to make. The phenomenon Merton named after Matthew was described more accurately about six hundred years earlier, by Daniel, in Aramaic.

When Daniel interprets the king’s dream, he opens with a blessing: יָהֵב חָכְמְתָא לְחַכִּימִין וּמַנְדְּעָא לְיָדְעֵי בִינָה, “He gives wisdom to the wise, and knowledge to those who already understand” (Daniel 2:21).

Read it the way the Matthew effect is usually read and it sounds just as unfair: wisdom handed to the people who already have it. The rabbis noticed. The Talmud (Berakhot 55a) says it flatly. The Holy One grants wisdom only to one who already has wisdom, and it cites this exact verse.

But the commentators flip it. A Roman noblewoman once challenged Rabbi Yose ben Halafta on precisely this point: surely God should give wisdom to fools, since they’re the ones who need it. He answered with a question. If two people came to you for a loan, one rich and one poor, which would you lend to? The rich one, she said, because he can pay it back. You’ve answered your own question, he told her (Midrash Tanchuma, Vayakhel). Give wisdom to a fool and he wastes it in the bathhouse. Give it to someone prepared to hold it and they build something.

Daniel isn’t talking about credit. He’s talking about capacity. Wisdom is lent to whoever has built a vessel that can hold it. Access was never the constraint. The vessel is.

Which is exactly why AI doesn’t level the field the way it appears to. AI hands everyone the surface and nothing underneath it. It floods you with access and leaves untouched the foundation that decides whether any of that access turns into something real. When everyone drinks from the same firehose, the thing that matters is who has somewhere to put the water. The dabbler with infinite knowledge at his fingertips still can’t hold it. If anything, the Daniel effect gets stronger in the AI age. Depth was always the real moat, and now it’s close to the only one left.

How do you escape a cold-start problem with no audience?

You don’t wait for the recognition. You can’t, because waiting is the trap. The only way out of the empty state is to manufacture your way out of it: show up, publish, build your presence deliberately, do the work before anyone is watching. Recognition comes after that, never before it. Every post you write does two things at once. It adds to the presence you don’t yet control, and it adds a layer to the vessel you do.

Loud Camel news

This week on Loud Camel, a tool that helps researchers get cited and recognized, I shipped exactly this idea into the product. The Reddit opportunities view used to go blank when there were no good threads to reply to, which is the worst thing you can show someone fighting a cold start. Now it always proposes at least one post to publish, with angle-level dedup so even saturated accounts keep getting fresh angles instead of an empty screen. The honest version of an empty state isn’t “nothing here”, it is “here is the next thing you can do”.

Frequently Asked Question

What is the cheapest way to start building visibility before anyone is paying attention?

Start with the cheapest threshold-crossing action there is: profile hygiene. Open your Google Scholar profile, count the papers listed, and compare against your CV. Most researchers find one to three papers missing or duplicated, and every duplicate quietly splits your credit between two half-yous, which is the Matthew engine working against you. Loud Camel automates this kind of low-effort, high-leverage upkeep on a recurring schedule, but you can do the first pass yourself in about ten minutes.

Takeaway

If you are staring at an empty dashboard, no audience and no track record, don’t wait to be noticed before you act. Make the first deposits now, while nobody is watching, because that is the only part of the system you actually control.

The ‘not ready to share’ antipattern

My friend and mentor Danny Lieberman writes an excellent newsletter about antipatterns: the moves people make instinctively that quietly cost them (https://substack.com/@dannylieberman). This post is in that spirit. The antipattern: keeping important work to yourself until it is ready. The fix turns out to be the thing the old saying tells you not to do.

When is your work actually ready to share?

The instinct is universal. When people work on something they consider important and big, they retreat into a shell and wait for the work to be done before they show it to anyone. A report for leadership. A presentation. A new product. A Python module. A pitch deck. The instinct is the same: I will share when it is ready.

There is a saying in many languages: do not show half-done work to a donkey. It sounds like discipline. I think it is one of the more harmful rules people carry around. It tells you to optimize for not looking foolish today, while saying nothing about whether your final product will be any good.

A donkey, the audience the saying tells you to fear.

“Show me your work”

This is the trap the donkey saying sets. It tells you the audience is the problem. Show your work only to people who can already see what you see. Otherwise they will misread, miss the point, ask a question whose answer is on page two. They will. That is the feature, not the bug. The “donkey” from the saying, the reader you were told to hide rough work from, is the most useful reader you have. They cannot see the picture you carry in your head, which means they will show you where it fails outside it.

What sharing rough work actually gets you

If the legal or IP situation allows, share your work long before you think it is ready. The half-done draft. The rough plot. The function that almost compiles. The demo with three broken screens.

Most of the feedback will be off-target. You will think, this person did not get it. Sometimes they did not. More often, they got something you stopped noticing: that the framing was not clear, that the order of the argument was confusing, that the assumption you treated as obvious is not obvious to anyone else. You think you know what you know, but you might not know what you know.

The embarrassment cost of sharing rough work is small and one-time. The cost of polishing the wrong thing is large and compounds.

So pick the piece of work you have been keeping in your shell because it is “not ready to share yet.” Find one person who will give you an honest reaction. Send it to them today, in the state it is in, with one sentence:

“I am still working on this and I do not know what it will be. Tell me what you see.”

You will get back something useful, often only one sentence. That sentence is worth more than another week alone with the draft.

If you are in academia and work on a paper, publish a draft on arxiv or preprints.org. You will timestamp your findings so nobody scoops you, and you will attract feedback that makes the review process smoother. Loud Camel, the tool I work on, helps you attract that feedback faster.

Why your acquaintances, not your closest friends, bring you the next opportunity

Question: what type of ties have better potential to help you in your career? Strong and close ties, or weak ones?

There is a Hebrew saying: כשיש קשרים לא צריך פרוטקציה. Roughly translated: when you have ties, you do not need pull. The word kesharim means connections, exactly what social scientists call social ties. Protektzia is the well-placed favor, the powerful patron who picks up the phone for you, the quiet override of the queue. The saying claims that a wide network of ordinary kesharim makes that patron unnecessary.

A sociologist named Mark Granovetter said something similar in formal terms in May 1973. His paper in the American Journal of Sociology, “The Strength of Weak Ties,” is one of the most-cited in social science. The twist: it is not your strongest ties that matter most for finding what you need. It is the weaker ones.

Why your closest people carry the least new information

Granovetter’s mechanism is simple. Your strongest ties tend to know each other and know what you know. If you have a strong tie to two people, the odds are good that those two have a strong tie to each other. You all go to the same events, share the same circle. The cluster ends up closed and densely overlapping. New information has nowhere new to enter from.

Acquaintances live in other clusters. They go to different events, work in different places, read different things. A weak tie acts as a bridge between you and a part of the world your strong ties never touch.

Figure 2 from Granovetter (1973). Solid lines are strong ties, dashed lines weak. The dashed bridges connect otherwise separate clusters.

What the job-finding numbers showed

Granovetter’s empirical study made the abstract argument concrete. He surveyed professional, technical, and managerial workers in Newton, Massachusetts who had recently changed jobs. Among those who found their job through a personal contact, only about 17% had been seeing that contact often. About 56% had seen them only occasionally, and 28% rarely. Most of the useful job leads were arriving from people on the edge of the person’s social life, not from the center.

How to put yourself near the next opportunity

The practical move is counterintuitive. If you want news, opportunities, or perspectives your inner circle does not already carry, do not lean harder on your closest people. They have already given you most of what they have. Spend time on the people you see twice a year. The colleague from a project five years ago. The acquaintance you barely know but quite like. Reply to the email you almost did not reply to. Show up at the meetup.

Loud Camel, the app I work on, does exactly that: it helps academics grow the network of weak ties their tight circle cannot give them.

The Hebrew saying gets to it in a single line. When you have ties, you do not need pull. So pick three people you used to be close to and barely speak with now. Send one of them a real message this week.

Is it ethical to use AI to promote your research?

“Is it ethical to use AI to generate content that promotes my research?”

A researcher asked me that recently. My answer: not only is it ethical. It is unethical not to.

“Of course you would say that, Boris. You founded Loud Camel, a service that uses AI to promote academics’ research and careers.”

Fair. Loud Camel is a tool that helps researchers get cited and recognized, and yes, I sell it. So hear me out, and judge the argument, not the messenger.

The research already shows that promotion works

Start with the evidence. A large body of research shows that scientists who actively promote their work do better. They get cited more, read more, and noticed more, often for the same findings as quieter colleagues. You can dislike that attention works this way. It still works this way.

Good science means putting your claim on the line

Karl Popper, the philosopher of science, argued that a serious scientific claim sticks its neck out. It makes refutable predictions. In Hebrew we call this ניבוי מסתכן, a risk-taking prediction. Popper was describing theories, not promotion, so this is an analogy and not a quote. But the instinct carries over. A claim worth making is one you are willing to state in public, clearly enough that it can be challenged and, if it is wrong, refuted.

Karl Popper. Photo: Wikimedia Commons.

Nassim Taleb, in Skin in the Game, makes the neighboring point. You should bear the consequences of your claims. If you are not willing to attach your name to a finding and let the world push back, you have not finished the job. Promoting your work honestly is a form of skin in the game. It is you saying, out loud, that you stand behind this.

The real risk is leaving the floor to the loud and the wrong

Now the part I care about most. If you think that promoting your research with AI is not ethical, think about this. You are an ethical person. You value integrity and careful claims. Not everyone does. Some people produce shoddy or dishonest work, and those people will not stay shy. They will use AI to make as much noise as they can.

So if that is true, staying quiet is not neutral. It is a choice with a cost. If the careful researchers hold back on principle, the reckless ones inherit the microphone. It is your responsibility, to your field and to the public, to make sure their voices are not the only ones heard in the air.

Why the wording of your abstract affects how often you get cited

The words you choose for your abstract are linked to how often your paper gets cited. A study of 136,615 papers in Nature, Science, and PNAS found that abstracts with more promotional language drew more citations, more full-text views, more media coverage, and higher Altmetric scores. Same journals. Same peer review. The wording still moved the numbers.

What counts as promotional language in an abstract?

Promotional language is wording that frames a finding as important, novel, or impactful. Think of words like unprecedented, remarkable, and first. Olga Stavrova and colleagues coded this language across abstracts published in three of the most selective journals in science between 1991 and 2023. They then linked the amount of promotional language in each abstract to that paper’s later citations, reads, and online attention.

Does the wording really matter?

The pattern held across every outcome they measured. More promotional language went with more citations, more full-text views, more news mentions, and higher Altmetric scores. These are papers that already cleared the highest bar in publishing. Even among them, framing predicted attention.

One honest caveat. This is a correlation, not a controlled experiment, so authors who use confident wording may differ in other ways too. But the size of the dataset and the consistency across four separate outcomes make the link hard to wave away. The same study also found that promotional language widened the gender gap in impact rather than closing it, so framing is a lever, not a fix for structural bias.

What to do with your next abstract

Write your abstract so a busy reader grasps why the work matters, not only what you did. Lead with the result. Say plainly what is new. Use concrete, confident language where the evidence earns it, and drop words the data cannot support. The goal is not hype. It is clarity that travels past the people already in your subfield.

Which leaves one question. If the words around your work change how often it gets cited, who is helping you choose them, across your abstract, your profile, and everywhere people search for you? For a growing number of researchers, the answer is Loud Camel, a tool that helps researchers get cited and recognized.

When Your Code Is Avoiding the Question Your Startup Needs Answered

I am a developer. For most of the past month, I used the one thing I am best at to avoid the one thing my company actually needs. There is a way to procrastinate that looks exactly like hard work, and a tidy commit history is its favorite disguise.

Why clean code is not progress before your first customer

My company exists to answer a single question right now: will researchers pay to make their work impossible to overlook? Not whether the code is clean. Not whether the architecture scales. Not whether the landing page is elegant. Will a stranger I have never met find this valuable enough to pay for it. That is the whole game for the first six months. Validation, not scale.

Here is what one of those weeks looked like in the commit log. About 22,000 lines added, 13,000 removed, 90 commits, 37 pull requests. By any engineering measure, a productive week. Then I read the diff more closely. Roughly 70% of it was modularization and deleting dead code. Real work. Genuinely useful. And almost entirely beside the point.

None of it moved the only number that matters in a validation phase. The home page held visitors for about two minutes and converted zero of them. Stranger signups: zero. Paying customers: still zero. The codebase got measurably better while the question the business is supposed to answer stayed exactly where it started.

Why technical founders code instead of talking to customers

Code gives you clean, immediate, impersonal feedback. It compiles or it does not. The tests pass or they fail. Nothing about a failing test feels like a judgment of you. A cold email to a researcher you admire is the opposite. You send it into silence, and silence about work you have poured yourself into reads like a verdict. So you open the editor instead. Refactoring is safe. Asking a stranger for money is not.

Engineering also produces beautiful evidence of effort. Commits, green checkmarks, a tidy diff. You end the day able to point at something. Outreach on a slow week produces a sent folder and no replies. One of those feels like progress. Only one of them is, when the open question is whether anyone wants the thing.

Why writing a bad habit down once does not fix it

The first time I caught this, I wrote it in a weekly review and assumed that would settle it. It did not. I did the same thing the next week, and the week after. Eventually I added a permanent line to every weekly plan: “Engineering-as-avoidance watch.” A standing reminder, because the pull is standing. This is not a one-time mistake you correct and move past. It is a default you have to keep choosing against, every single week.

Why building instead of validating is the most expensive choice

The avoidance can hide the answer. Every week I spend building instead of asking is a week I do not learn whether anyone will pay. If the answer turns out to be no, I would much rather know now, cheaply, than discover it after another month of immaculate refactoring. A perfect codebase for a product nobody wants is the most expensive possible way to not find out.

So I changed the deliverable. For one week I was not allowed to ship a feature. The output was conversations: a free guide that handed researchers something useful with no signup wall, a handful of sharper cold emails, and three real interviews with people who agreed to talk. If those produce signal, the pattern is behind me. If they produce nothing, then the pattern was never just procrastination. It was the diagnosis. Either way, I find out, which was always the only point.

Loud Camel news

Last week Loud Camel, a tool that helps researchers get cited and recognized, shipped no new features on purpose. The slot a feature usually takes went to conversations instead: a no-signup guide, a few sharper cold emails, and three booked interviews. The note for any founder reading this is simple: if “talk to strangers” is not given the same weight on the plan as a feature, the safer work wins every time, and you can lose a month to it before you notice.

Frequently Asked Question

Is shipping the product the same as validating it? No, and the gap is where founders get stuck. Building tests whether you can make the thing; validation tests whether anyone will pay for it, and only the second one tells you if the company should exist. This is also the bet behind Loud Camel: its handbook documents nine visibility tactics drawn from the peer-reviewed literature on how recognition actually accrues, and the product runs those tactics for researchers on a recurring schedule, so the question stops being “did I do the work” and becomes “did the right people notice.”

Takeaway

If you are a founder before your first dollar of revenue, the work that feels most productive is often the work that protects you from the answer. Go get the answer.

When your LLM pipeline silently returns zero

One Sunday morning the daily scan ran for a user of Loud Camel, a tool that helps academics promote their research and get cited. It came back clean: a couple dozen items scored, zero relevant, zero results delivered. That looked like the system telling me there were no good matches this week. It was the system screaming, with nothing logged.

The silent-but-deadly failure mode

Pardon the analogy. Silent failures in LLM pipelines work like the worst farts in an elevator: nothing audible, nothing on the surface, then you notice the room has emptied. The LLM call returned. The parser returned a Python dict. Every type check passed. The number returned was zero, and zero looked like the truth.

What actually went wrong

The model hit its max_tokens cap and the response was truncated mid-string. No closing brace, no closing fence. The JSON parser had a clever repair fallback: it scanned for key-value pairs regardless of nesting depth and reassembled them into a flat dict. The repair returned an object that was technically dict-shaped but contained the wrong keys, all from the truncated inner level of the structure. The consumer iterated, found nothing it recognized, defaulted every item to a score of zero. The dashboard showed zero relevant, the user got an empty scan, and the cost line read like everything was normal.

Two days later the same shape showed up in a different LLM call site. The model output truncated at a different limit, the parser returned a dict-shaped object with the wrong keys, the consumer produced zero results. The day after, a third call site failed the same way. Three places. One bug class. No alarms.

How to make a silent failure loud

Two cheap defenses, neither of which I had on Sunday morning.

First, the parser cannot be allowed to lie about shape. A truncated array should return None or the complete prefix, never an object. A truncated nested object should return only the outer-level keys that were complete, never the inner ones hoisted up. The fix is unit tests at the parser boundary that assert this shape contract. Zero LLM cost. Deterministic.

Second, the consumer must validate the shape before defaulting to zero. If the function expects a dict keyed by request IDs, it should check that the returned keys are request IDs and warn loudly if they are not. A single line that reads ‘scored 0 of N items, response shape unexpected’ would have turned a four-day silent outage into a four-minute fix.

Why this is the bug class to invest in

LLM call sites multiply faster than you can audit them. Every prompt change, every model change, every batch size change opens a new path to the same failure. Patching each call site after it bleeds is stop-gap engineering. The structural defense is to make the parser refuse to lie and the consumer refuse to be silent. Both run in tests, in milliseconds, with no token cost. Both would have caught all three of my outages before any user saw a zero.

Silent but deadly is funny once. It is not funny when a real user is waiting on an empty scan for a week.

Not a Bug but a Feature

A common reaction to data on research visibility goes something like: “Most papers go unread? The whole academic system is broken.” It’s an understandable response. But I think it gets the diagnosis wrong.

Science has always been social. Robert Merton, writing in the 1940s, identified communalism as one of the constitutive norms of science: findings are the common heritage of the scientific community, and the obligation to communicate them is built into what science is. A result locked in a desk drawer isn’t doing science. Bruno Latour put it more provocatively: a claim doesn’t really become a fact until other researchers take it up, cite it, build on it, argue with it. Circulation isn’t downstream of knowledge production — it’s part of it.

This is why I push back on the “broken system” framing. If I publish a paper and it moves no one — no reader, no citation, no conversation — did I actually contribute something to the field? Humans are social creatures. Science is a human endeavor. The need to find your audience isn’t a flaw in the system; it’s closer to the whole point.

Where things genuinely do go wrong is the Matthew effect, also Merton’s term: attention compounds. Established researchers get seen, which gets them cited, which gets them seen more. Early-career researchers, with networks still forming, fall on the wrong side of that feedback loop — not because their work is weaker, but because nobody knows it exists yet.

So the problem isn’t that visibility matters. The problem is that visibility is unequally distributed in ways that have little to do with the quality of the work. Lowering the cost of strategic outreach — helping good work find the people who should know about it — isn’t gaming the system. It’s leveling it.

References

Latour, Bruno. Science in Action: How to Follow Scientists and Engineers Through Society. Cambridge, MA: Harvard University Press, 1987.

Merton, Robert K. “The Matthew Effect in Science.” Science 159, no. 3810 (1968): 56–63.

Merton, Robert K. The Sociology of Science: Theoretical and Empirical Investigations. Chicago: University of Chicago Press, 1973.

Customers see your tunnel vision before you do

You cannot detect tunnel vision from inside the tunnel. The light at the end is right there, but you stopped looking up from the rails. I learned this last week when an early user caught two failures in my product that I had built, reviewed, and shipped.

What I shipped

An early user opened my product last week. Loud Camel is a tool that helps researchers get cited and recognized. The first paper it surfaced was attributed to the wrong author. The named researcher had not written that paper.

Then they flagged something heavier. The cold-email drafts the product writes for users imply the sender read the recipient’s paper. The sender did not. I built that flow. I reviewed those drafts. I shipped them anyway.

How I lost the star

When I started Loud Camel I told myself integrity was the north star. Every recommendation honest. Every email truthful. Then I spent four months deep in OpenAlex joins, email parsing, and pipeline plumbing. The star drifted out of my field of view. I was looking at the code.

This is the bug in founder cognition that scares me most. The thing I cared about the most became the thing I stopped checking. Not because I stopped caring. Because I stopped looking.

Why founders cannot audit themselves

I have tried the standard remedies. Weekly review of priorities. A pinned list of values on the wall. Asking myself whether I am building what I said I would build. None of it pulled me out. The frame you use to evaluate the work is the same frame that built the work. You cannot audit yourself from inside the tunnel.

What customers see that you cannot

The user who writes to say ‘this looks wrong’ pulls you out. The teammate who says ‘wait, are we sure?’ pulls you out. They see the product the way you wanted it seen. You see it the way you currently see it. The customer sees what you stopped seeing.

If you are building something, schedule the conversations that yank you back to the surface. Treat them as a check on whether you still recognize the product you wanted to make.

What I am changing

I am adding a validation step that confirms the attributed author actually appears in the paper’s author list before any recommendation is surfaced. I am rewriting the cold-email drafts so they do not pretend the sender read what the sender did not read. I am writing back to every user who flagged something and thanking them.

The next time the north star drifts, I want a user to notice before me. I would rather hear it from them at month four than ship past it for another four months alone.

LLMs sharpen the Matthew effect in citations

The Matthew effect is a 1968 observation by sociologist Robert K. Merton. In science, credit accrues to people who already have it. Two researchers do the same work; the famous one gets cited, the unknown one is footnoted if they are lucky. Merton took the phrase from the gospel of Matthew: “For unto every one that hath shall be given.” In citation data it shows up as a power law. A small number of papers collect most of the citations, and once a paper joins the famous tier, the rate at which it accrues new citations only rises.

A new line of work asks what happens to that dynamic when the tool suggesting citations is an LLM.

The experimental finding

Algaba and colleagues fed GPT-4, GPT-4o, and Claude 3.5 the abstracts of 166 ML papers from AAAI, NeurIPS, ICML, and ICLR, and asked each model to suggest references. The LLM-suggested references had much higher median citation counts than the papers’ own references, even after controlling for publication year, venue, title length, and author count. A follow-up scaled the test to ten thousand papers and around 275,000 generated references across domains. The bias toward already-highly-cited, shorter-titled, somewhat more recent work persisted, even though the suggestions looked semantically appropriate inside existing citation graphs.

What this means for a working researcher

LLMs are pattern matchers over a corpus where the Matthew effect was already baked in. The thing they are good at, returning the most plausible reference for an idea, is exactly the thing that surfaces the already-famous paper over the equally-valid lesser-known one. Wieczorek and co-authors call this the status-quo scenario for LLM use in literature search: existing inequalities reproduce, possibly faster.

The career-level evidence is not in yet. Nobody has shown that LLM use is, on its own, tilting hiring, tenure, or funding outcomes. But citations feed those decisions, and citations are the channel where the bias has now been measured.

Treat the first three references your LLM suggests as a starting list, not the final list.

P.S. Two centuries before the gospel of Matthew, the Book of Daniel (2:21) made the same point in Aramaic: יָהֵב חָכְמְתָא לְחַכִּימִין וּמַנְדְּעָא לְיָדְעֵי בִינָה. “He gives wisdom to the wise, and knowledge to those who know understanding.” The traditional reading is that wisdom flows to those who already have it. Maybe Merton should have called it the Daniel effect. ¯_(ツ)_/¯

References

Algaba, A., Mazijn, C., Holst, V., Tori, F., Wenmackers, S., & Ginis, V. (2025). Large Language Models Reflect Human Citation Patterns with a Heightened Citation Bias. In Proceedings of NAACL 2025, 6844-6853.

Algaba, A., Holst, V., Tori, F., Mobini, M., Verbeken, B., Wenmackers, S., & Ginis, V. (2025). How Deep Do Large Language Models Internalize Scientific Literature and Citation Practices? arXiv:2504.02767.

Baert, P., Dorschel, R., Hall, M., Higgins, I., McPherson, E., & Philip, S. (2025). Dialogues Towards Sociologies of Generative AI. Social Science Computer Review (online first).

Wieczorek, O., Steinhardt, I., Schmidt, R., Mauermeister, S., & Schneijderberg, C. (2024). The Bot Delusion: Large Language Models and Anticipated Consequences for Academics’ Publication and Citation Behavior. Futures 166: 103537.

An Illustrated Guide to Academic Publishing

A short story about how a paper is born — and why almost nobody will read it.

Meet a researcher. Smart. Curious. Slightly overcaffeinated.

This is you. Or someone like you. You went into research because you wanted to understand something the rest of the world hasn’t figured out yet. You probably didn’t go in for the money. You definitely didn’t go in for the email volume.

Your job, more or less, is to take ideas out of your head and put them into the heads of other people. The path between those two points is longer than anyone tells you on day one. Here is what it looks like.

It starts with a speck

Somewhere in there, an idea.

Every paper begins as a tiny speck — a hunch, a stray sentence in someone else’s discussion section, an experimental result that doesn’t quite fit the textbook.

At this stage, the idea is small enough to fit on the back of a napkin and not quite small enough to ignore. You decide to keep it.

Let’s zoom in

The speck, up close. Still mostly empty space.

Up close, the idea is even less impressive than it looked from across the room. It is small, it is fuzzy, and it is surrounded by an enormous quantity of ‘I’m not sure yet.’

That’s fine. Most things start that way. Now you go to work on it.

You read. You think. You read some more.

The speck grows a little. Reading helps.

You read papers. You read papers that cite those papers. You read papers that those papers tried to refute. You scribble in margins. You stare at the ceiling. You explain the idea to a friend who is too polite to interrupt.

Slowly, the speck gets bigger. Not because you added anything from outside — but because you finally understand what was already there.

Literature review. Proposal. Funding.

Bureaucracy arrives.

Now things turn administrative. You write a literature review that proves you are not the first person on the planet to have a thought. You write a proposal explaining what you would like to do and why somebody should pay for it.

Then you wait. The idea, meanwhile, keeps growing — partly because you keep thinking about it, partly because explaining it ten times to ten different review panels forces you to make it sharper.

Collect the data. Run the experiments. Ask for help.

The speck is now noticeably less speck-like.

Funding (finally) comes through, or you proceed without it. Either way, the real work starts: experiments that don’t work, code that doesn’t run, instruments that pick today, of all days, to break.

You ask for help. You email someone you’ve never met. You buy a colleague coffee in exchange for thirty minutes of their attention. You learn, perhaps for the first time, that research is mostly other people.

Draft. Review. Refine. Polish. Repeat.

Most of your head is now occupied by one idea.

You write a first draft. It is bad. You knew it would be bad, but it is bad in ways you did not predict. You rewrite. Then you rewrite the rewrite.

By now the idea has filled almost everything in your head. You catch yourself thinking about it in line at the supermarket. You think about it in the shower. Your friends have started to change the subject.

You submit.

There is no other thought.

When you finally click ‘submit,’ there is nothing else inside your head. The idea has taken up all the space. You refresh the submission portal. You refresh it again. You explain to family members what ‘desk reject’ means. They nod politely.

Then the reviewers reply.

They have remarks.

Reviewer 1 is generous. Reviewer 2 is not. Reviewer 3 appears to have read a different paper, possibly in a different field. You read their comments three times — once for content, once out of anger, and once to actually take notes.

You revise. You respond. You explain, in the most patient voice you can summon in writing, why their kind suggestion would in fact destroy the paper.

Accepted.

Pride. Quite a lot of it, actually.

The email arrives. You read it twice to make sure. You tell your partner. You tell your supervisor. You tell, with somewhat less success, the person at the next desk who has been watching you suffer for the past eighteen months.

This is you. Proud and happy.

Take the afternoon. You earned it. The paper is out. Your name is on it. Somewhere in a server in Amsterdam, a row has been added to a database.

Now zoom out.

Find yourself. Take your time.

Here is what almost nobody tells you. You are not the only person who just published. Roughly five million peer-reviewed papers go out into the world every year. Each one is somebody’s two-year speck. Each one represents somebody’s afternoon of pride.

Most of them are read by almost no one. Half of all published papers are cited fewer than three times. A large fraction are never cited at all. The median paper has roughly the impact of a tweet that nobody retweeted.

That is the part that hurts. The work was real. The idea was real. The result was real. The visibility was not.

Your research is good. But nobody knows it.

The problem isn’t the quality of the work. The problem is that ‘publish and wait’ stopped working sometime around when search engines started ranking by engagement and AI assistants started answering questions without showing their sources.

Citations, grants, collaborations, invitations to give talks — they all start with someone, somewhere, encountering your work and remembering it. That encounter no longer happens on its own.

We built Loud Camel for the people in that crowd. Once a month, we put together a short brief: who in your field has started working on something near your topic, which conversations are happening in places that LLMs and search engines actually read, which dormant contacts are worth a two-line reconnect. You decide what to send. We just make sure you have something to send.

loudcamel.com — reclaim the visibility your research deserves.

Where you debut probably decides where you stay

A 2018 paper from Albert-László Barabási’s group (Fraiberger, Sinatra and colleagues) maps the global art world as a single network. Barabási is the network scientist who introduced scale-free networks two decades ago and runs labs at Northeastern and Harvard; his book The Formula: The Universal Laws of Success is the readable distillation of this whole research line. If any of what follows surprises you, pick it up.

The team tracked 496,354 artists across 16,002 galleries and 7,568 museums between 1980 and 2016, drawing an edge between any two institutions whenever an artist exhibited at one and then at the other. The result is a dense Western core (MoMA, MET, Guggenheim, Tate, Pompidou) with a ring of regional clusters around it: Japanese, Brazilian, Australian, Eastern European. The links between those clusters and the core are thin.

What an artist’s first five shows predict

The authors then take only the first five exhibitions of each artist and use them to predict the next thirty. A model that respects those five does it accurately. A memoryless model fails.

Curators choose new artists by looking at the curators who chose them before. The first tier you land in becomes the reference set that does most of the later picking for you.

What this means outside the gallery world

The same shape probably reproduces in any career path that flows through institutions and gatekeepers. First lab. First publication. First conference. First podcast. Each has a core and a periphery, and the gap between them takes time to cross.

If you can afford to be patient about exactly one career choice, make it the first one.

I built the wrong dashboard for two weeks

When I worked at Automattic, on parts of WordPress.com and Jetpack, we used to say that counting things is hard. With time I realized the harder problem is one rung up: counting the right things is even harder. Most teams solve the first problem, define the metric carefully, and never notice the second. The metric they defined is not the one that mattered.

I walked into a clean version of this on my own product. I built an outreach tool last month. The first thing I did was sit and watch it work. Emails sent today, emails queued, emails waiting for the morning batch. The numbers moved when I clicked things. It felt productive.

Two weeks in, I was still sending email and I had no idea who had read any of it.

How to tell when a metric is the wrong half of the loop

The reason this is so easy to get wrong is structural. Anything I do inside my own software produces a clean record on the way out. I click Send, my code notes the click, the counter goes up. The action and the metric are in the same loop, on the same machine, written by the same people.

What happens to the email after that is on someone else’s screen. It might land in a folder. It might be skimmed. It might sit unread in a tab that stays open all afternoon. Each step adds latency, ambiguity, and another team’s instrumentation choices. By the time any of it makes it back to me, it lives in a different table, behind a different filter, on a different page. The path is longer and the data is less clean.

Most teams do not bother to bring it back at all. They are not lying. They are measuring what is easy.

The shortcut for spotting this is to ask, of any number on a dashboard, who created the event that made the number move. If the answer is “I did”, or “my team did”, or “my system did”, the metric is on the inside of the loop. If the answer is “the person we are trying to reach”, the metric is on the outside. Most dashboards are 90% inside-the-loop because that is where the data is cheap.

What I changed this week

This week I pulled some of the response signal up to where I was already looking. I did not invent a metric. I just stopped hiding the ones I had. The contact card now tells me when I last drafted to a person and when I last marked them as contacted, both in plain language above the action buttons. The admin view for outbound links shows whether a link has been clicked, is still waiting, or has expired, with relative timestamps, instead of leaving me to grep logs.

The interesting result is what stopped happening, not what started. I stopped sending followups to people I had already reached. I stopped sending followups to people I had just contacted. None of this came from new resolve. The right number sitting in the right place did most of the work.

Loud Camel news

The product I have been describing is Loud Camel, a tool that helps researchers get cited and recognized. The contact-card recency lines and the magic-link status panel both shipped this week, alongside a first cut of an admin-driven prospect outreach flow. Each is small. Together they move the screen closer to the one I should have built first.

Takeaway

Open the dashboard you check every morning. If most of what is on it is things you did, the screen is telling you about your week and not about the world. The fix is rarely a new metric. It is usually a number that already exists somewhere, moved one screen over to where the next decision happens.

Rules don’t change how people write. Contrasts do.

If you want someone to write, design, or build differently, stop writing longer rules. Produce the alternative they can compare against. The contrast does the work the rule could not. A new Columbia paper gives this principle a name and the data.

What the paper found

Li et al. (2026) studied scientists trying to translate research into social media posts. Standard advice told them to use relatable examples, walkthroughs, and personal language. Most ignored it or actively resisted. Abstract rules don’t tell anyone what “personal” looks like in their own field.

The researchers tried something else. Two versions of the same explanation, side by side. One warm, one clinical. Same science, different style.

Behavior changed. Even scientists hostile to “personal language” picked it up after seeing the dry version next to the warm one. One participant said: “I never would have thought to say this.” That line became the title of the paper.

Why contrasts work and rules don’t

Recognition is cheaper than recall. Spotting which version reads better is fast. Generating a “more relatable” version from a rule is hard work. The paper invokes a basic usability principle: design for recognition, not recall. Most writing advice violates this.

Preferences are also a continuum, not a binary. There is no correct amount of personal language. People can only locate themselves on the spectrum once they see both ends.

What to do instead

When a PM tells a designer “make it cleaner,” nothing happens. When the PM produces a quick alternative mock and asks “this versus yours, what do we lose,” something happens. Same for coaching reports, editing PRDs, and prompting LLMs. “Make it better” produces hedged variants. “Here is your draft and here is a tighter version, pick which reads truer” produces change.

Style guides are rules. Diffs are contrasts. The work is in producing the alternative.

The worst way to visualize geographic data

Country choropleth maps are the wrong chart for visit counts. Or for any kind of count. They encode magnitude as land area, but the question you actually want answered is “where are my users?”, not “which country owns the most square kilometers?” The two have almost nothing in common.

I recently launched Loud Camel, a tool that helps researchers get cited and recognized. I opened the Google Analytics geographic report and got the “before” map below.

Look at it for five seconds. Cover the title. What are the top five countries by active users?

You probably said the United States. Maybe Canada and Russia. The blue blobs are big and obvious.

Now find Israel.

You can’t. It is one or two pixels somewhere east of the Mediterranean. Israel is currently the country with the most active users on the site. It is the leader. The map hides the leader.

Why country maps mislead about counts

A choropleth fills each country with a color whose intensity encodes a value. In theory the reader compares colors. In practice the reader compares areas, because area is the loudest visual signal on the page.

A country with a huge area and a low value looks important. A country with a tiny area and a high value disappears. The reader’s eye correlates magnitude with land mass, not with the data.

This works only when geographic proximity matters: epidemiology, regional logistics, climate maps. Visit counts have no spatial structure. A user in Texas is not “close” to a user in Mexico in any way that matters to the data. The map adds noise, not insight.

Use a sorted bar chart instead

The same data, sorted from largest to smallest as a horizontal bar chart, looks less sophisticated, but reads in one second.

Bar length encodes the value directly. The reader’s eye runs top-down by magnitude. Israel is at the top because Israel has the most users. The United States is second because it has the second-most. There is nothing to interpret.

The numbers in the chart are illustrative; the ordering is not.

What’s with the camel?

It is my new service. If you have an academic career and want to advance it, talk to me.

The hardest part of being a solo founder wasn’t what I thought

The hardest part of running a one-person company is not the workload. It is choosing the right work.

What I expected

When I started, I assumed the hard part would be wearing every hat. CEO. CTO. CMO. VP Product. Four jobs in one chair, no one to delegate to, no one to escalate to.

All of that while working full time as a lecturer and researcher at a college.

The workload is real. It is not the bottleneck.

What is actually hard

A solo founder is one person, and that person has a default mode. Mine is building. I want to ship features, fix bugs, and tighten infrastructure until it hums.

Building feels productive. It is measurable. It is satisfying. At the end of a coding day the diff is real, the bug list is shorter, the system is leaner.

The work that pays the bills feels like distraction

Sales conversations. Watching the market. Learning what customers actually need. Talking to people instead of typing into a terminal.

This work has no diff at the end of the day. No ticket closed, no graph that moves on a dashboard you control. It feels soft, slow, like you are getting away with something.

It is also the work that decides whether the product survives.

The real lesson

A perfectly built product nobody wants is worse than a half-built one customers are pulling out of your hands.

The hardest part of being solo was not doing everything. It was resisting the role I am best at when another role needed me more.

Building is the comfortable seat. Talking to customers is the uncomfortable one. The discipline is to spend more time in the uncomfortable seat than feels natural.

A small weekly check

Each Friday I ask one question: did I spend more time this week with code or with customers?

If the answer is always code, that is the bug to fix.

AI Articles Overtook Human Articles. That Is Not Automatically Bad

More AI generated articles than human written articles is not automatically a decline. It can be a transition in media production, similar to printing press replacing manual copying and photography replacing painting for documentation.

What the Reddit chart claims

A post on r/dataisbeautiful presents a crossover point where AI generated articles overtake human written articles. The exact dataset details need scrutiny, but the high level signal is clear enough to discuss. Cheap content generation is scaling faster than manual writing output.

That pattern is unsurprising. When production cost drops by orders of magnitude, volume usually explodes. The same thing happened when printing moved reproduction from skilled scribes to press operators. The same thing happened when cameras made visual capture fast and repeatable.

Why volume growth is not the core problem

A larger supply of text does not force lower quality consumption. Distribution systems decide what people see. Ranking models, recommendation systems, editorial choices, and user habits decide which items get attention. The bottleneck moved from production to filtering.

In that environment, the right question is not whether AI text exists. The right question is whether readers can quickly identify what is useful, original, and trustworthy. If curation improves, higher volume can increase discovery. If curation fails, noise wins.

How this connects to zero-directionality

This framing aligns with our preprint, “Either Companionship or Death: Zero-Directionality and the Structural Disappearance of the Social Other” (https://www.preprints.org/manuscript/202603.0382). The core claim is that many digital interactions have crossed into zero-directionality. In these interactions, the social other is absent and the machine becomes the communicative counterpart.

Seen through that lens, the AI article crossover is not only a content story. It is a structural story about who is in the loop. When drafting, ranking, and recommendation shift toward human-machine loops without enough human mediation, the risk is substitution. When AI helps humans evaluate, compare, and connect with other humans, the result can be companionship rather than displacement.

A better frame for the next year

The historical analogy is practical. Printing did not kill books. It multiplied access and shifted value toward selection, editing, and distribution. Photography did not kill art. It changed what painting was for. AI writing is likely to do the same. Routine drafting and templated explainers become cheaper. Human judgment, synthesis, and social accountability become more valuable.

So I would not argue that overtaking is bad by default. I would argue that institutions and creators need better quality signals. Provenance labels, source transparency, reputation systems, and stronger editorial standards matter more now than before.

What would change my mind

The strongest counterargument is not philosophical. It is empirical. If we observe that domains with high AI article penetration show lower factual accuracy, lower source diversity, and lower reader trust over time, then the transition is harmful in practice. The same applies if search and social ranking systems consistently reward synthetic repetition over original reporting.

This is measurable. Track correction rates, citation quality, source overlap, and time to find a reliable answer. Compare domains with different AI adoption levels. If those metrics degrade and stay degraded, abundance is hurting knowledge ecosystems. If they improve, abundance is likely helping people find and learn faster.

That is why panic is not a strategy. Instrumentation is. The important work now is to define quality metrics, publish them openly, and hold platforms accountable for them.

Takeaway

When machine output surpasses human output, panic is understandable. Better filtering is the real leverage point. Build that, and abundance becomes useful instead of overwhelming.

Simplification and ultra-personalization: two responses to a harsh critique

I shipped a feature-rich landing page for my product. About 100 unique visitors came through. Two signed up. The signal was loud; I almost missed it. A friendly but blunt critique from an opinion leader in the field forced two changes I should have made earlier. One was to strip the public landing page until it had a single message. The other was to generate a different landing page for every prospect I plan to contact this week. Both ship today.

What I shipped first

Loud Camel, a tool that helps researchers get cited and recognized, went out with a landing page I was proud of. Three sections, several screenshots, multiple use-case framings, an explainer video, social proof. Every feature got airtime. Every audience got a paragraph. From the inside, the page felt thorough.

What the signal was

Around a hundred unique visitors hit that page during the soft launch. Two signed up. A two-percent conversion rate isn’t a disaster; for a paid SaaS landing page it’s defensible. For a free tool offering a fast first report, it’s a warning. Visitors who arrive curious and bounce without leaving an email are telling you something the analytics dashboard cannot quite spell out: they didn’t understand what to do, or they didn’t believe it would be worth the effort.

I noticed the number. I told myself the launch was soft, the audience was diffuse, the funnel needed time. I was wrong.

The critique

I asked an opinion leader in my field to look at the page. He took the time to do it properly. The note he sent back was direct: too much, too fast, no clear next step, the strongest claim buried in the third paragraph. The page was a writer’s gift to me, not a reader’s path to action.

That kind of critique only lands when you hear it from someone who has nothing to gain by being polite. I am very glad I asked.

Response one: strip the public page

The new public page contains less information than the old one. The headline states one outcome in plain words. The single CTA leads to one form. Anything that does not move a first-time visitor toward ‘is this for me, yes or no’ is gone. Some content moved to a separate page; most of it I cut.

Less material on the page is the easier change. Saying less is not the hard part.

Response two: ultra-personalize per prospect

The harder change is the one I’m running this week as an experiment. With guidance from a mentor in this process, I’m shipping a minimal landing page generated per prospect: their field in the headline, a single sentence promising one specific output, a single email input. Nothing else.

A personalized landing page for computational chemistry researchers. Same template, different field in the headline, different sample preview. The page is dynamically generated for each lead I contact this week. Google Analytics is wired through end-to-end. Each version shares its origin link only with one researcher; conversion gets tracked per page.

The bet: a stranger who arrives via a personal email from me is converted by relevance, not by features. If the headline names their field and the form takes ten seconds, I find out which copy works. If they bounce, I find out which copy doesn’t.

What I’m watching this week

Two metrics. First, signup rate per personalized page versus the simplified public page. Second, time-to-first-action: how fast a visitor who arrives leaves an email. Both numbers go into a Google Analytics dashboard I will read on Sunday.

The general lesson, ahead of the data: when 2% sign up, the page is doing half the work. Simplify until visitors can answer ‘is this for me?’ in under five seconds. If you can also tell them yes by name, do that.

When rigid blocs break, they break together

A rigid voting bloc is supposed to be the boring case. People vote the same way, election after election, because their identity tells them to. The interesting story, the textbook says, is what happens when the bloc weakens.

I want to argue the opposite. The interesting case is when a rigid bloc breaks — and it breaks at every address at once. That synchronized fracture is not the bloc weakening. It is the bloc working.

The puzzle

Israel’s ultra-Orthodox (Haredi) population is one of the most disciplined voting blocs in any democracy. Two parties — UTJ for Ashkenazi voters, Shas for Sephardi voters — have split that vote with 90-95% loyalty for decades. Cross-ethnic voting (a Sephardi voting UTJ, or an Ashkenazi voting Shas) is so rare it is treated as measurement noise.

Then, in the March 2021 election, that noise jumped to 12-19% of Sephardi voters in five different cities, all at once. By the next election it was gone again. The voters did not move house. The borders did not shift. Same people, same streets, one election of mass cross-ethnic switching, then back to baseline.

The chart

Cross-ethnic switching jumped from a typical 1-3% to 9-19% across geographically dispersed Haredi cities — within a single election cycle. Demographics cannot move that fast. Something else did.

Why this matters

There is a tempting reading: the bloc was finally weakening. Identity politics fading. Voters acting as individuals.

That reading is wrong, and the geography is what tells you so. Independent voters do not switch in five cities in the same election in the same direction. Independent voters do not return to their original party 13 months later. What you see in the chart is coordination, not autonomy.

The corpus evidence — about 58,000 Haredi forum and news items from those 13 months — points to a specific channel: senior rabbis, working through yeshiva networks, told Sephardi voters to vote UTJ that one cycle. They listened. When the directive softened, they returned.

The framework

I call this ‘rigidity with stress fractures.’ The same machinery that produces decades of unbroken loyalty — centralized authority, dense networks, voting framed as collective duty — is the machinery that produces an instant, country-wide swing when leaders ask for one. Rigidity and synchronized switching are not opposites. They are two outputs of the same system.

This generalizes. Any identity-based bloc with centralized authority, organizational reach, and a population trained to follow directives can do this. Italian Christian Democracy under bishop replacements (Lanzara et al., 2024). U.S. evangelical voter guides (Campbell et al., 2011). Ethnic broker networks across sub-Saharan Africa (Horowitz, 1985). The Haredi case is unusually clean because elections came every 6-12 months and let us watch the disruption and the recovery in the same window.

The takeaway

When you see a sudden swing inside a population that was supposed to be politically immobile, do not jump to ‘the bloc is fragmenting.’ Check first whether the swing is synchronized — same direction, same magnitude, multiple places, single window. If it is, you are not watching the bloc weaken. You are watching it obey.

The mechanism that holds the bloc together is the same one that can move it. Stability and discipline-driven volatility are not opposite diagnoses. They are the same diagnosis read at different speeds.

Promoted papers keep pulling ahead: what the Kudlow RCT looks like at 36 months

Most papers don’t fail because they’re bad. They fail because nobody reads them. That isn’t a complaint about readers — it’s a description of a roughly two-million-paper-a-year firehose where even strong work gets buried by default.

The interesting question is whether doing something about it measurably moves citations, or whether promotion is just optics. The cleanest answer we have comes from a randomized controlled trial by Kudlow, Brown and Eysenbach, published in JMIR in 2021. This post walks through what they actually found.

The design

3,200 articles from 64 peer-reviewed journals, eight subject areas ranging from the life sciences to the humanities. Journals were drawn from the top 20 by h5-index in each subject, then eight journals were randomly sampled per area so the study would not be confined to only the highest-impact outlets. Articles were block-randomized at the subject level — 1,600 to the intervention arm, 1,600 to the control.

The intervention itself was narrow: links to the 1,600 intervention articles were surfaced as sponsored recommendations across the TrendMD cross-publisher network (around 4,300 participating journals, ~121 million monthly readers) for six months. Total budget: US $9,600 — about $6 per article. After six months, promotion stopped. The researchers then quietly watched citations accrue for another thirty months.

What the paper shows

The chart below reproduces Table 3 from the paper: mean citations per article at 6, 12 and 36 months, comparing promoted vs control.

At 6 months — effectively during the promotion window — the promoted arm was ahead by a mean of 1.06 citations (p = .005). At 12 months, the gap was 5.06 (p < .001). At 36 months, thirty months after promotion ended, the gap had grown to 10.52 citations (95% CI 3.79–17.25, p = .001). In relative terms that is the headline 28% increase, but the absolute version is the more interesting number: the gap kept widening long after the intervention stopped.

A Cohen d of 0.11 tells you the per-article effect is small. That is honest and worth sitting with. What makes the finding substantial is not the size of the individual shift but the structural property: a modest, cheap, time-bounded intervention produced a citation gap that was still widening three years on. The authors also note that the entire cumulative distribution of the promoted arm was shifted to the right — the gain was not driven by a handful of lucky viral papers.

Why this is interesting

Two reasons.

First, causal inference. Most of the scholarly-visibility literature is correlational: popular authors get cited more, active social-media users get cited more, open-access papers may get cited more (the meta-analyses disagree). With evidence that weak, it is easy to conclude that promotion is a proxy for being good rather than a lever you can pull. Block-randomization at the subject level cuts through that problem.

Second, the mechanism the authors imply. Dissemination generates reads, reads generate downstream citations, and citations generate further visibility in citation-weighted ranking systems. A push at the early part of that curve compounds across years. Six months of promotion bought a three-year advantage that was still accumulating.

What to take away

If you have done good work and left it unpromoted because promotion feels indecorous, you are quietly taxing your own research. The 28% figure is a floor — TrendMD is one specific channel. Targeted outreach to adjacent labs, mailing lists, newsletters, conference circulation, and social posts probably compound on top.

Pick one paper this week that you admire and wish more people knew about. Send it to one person who should read it. Then do it again next week. The chart above is what consistent, unfashionable legwork looks like three years in.

When in some places work meetings start with people introducing their preferable pronouns, in Israel we start with a briefing about the closest bomb shelter. When in other places people skip important meetings to take care of their pet goats (true story), in Israel people join meetings on Zoom from Army Reserve Service during breaks from patrols or other duties.

I wrote about this last June. The war will end, I said, and when it does, hundreds of thousands of Israelis will come back – to work, to building, to a country in crisis. And crisis breeds resilience. They’ll come back hardened as fuck.

Nine months later, nothing has changed my mind. If anything, I’m more convinced. The people who learned to operate under rockets, who managed companies while covering for half the team on reserve duty, who wrote code between sirens – they are coming back with muscles that can’t be trained in peacetime. Extreme communication, chaos management, creative problem-solving under constraints that most people can’t imagine.

Good luck competing with them. Good luck competing with us.

After my previous post about Anthropic’s spider chart, several people asked me how I would actually go about fixing it. So here is how – with a single prompt.

I gave Claude the original graph and the following instruction, based on my C for Conclusion approach:

the visualization is not good. First, formalize the image into a single-sentence conclusion of up to 8 words.

Then, use the approach of Stephen Few and Edward Tufte, maximize the data-ink ratio, use Information layers (Jean-luc Doumont) to generate the graph based on the same data that is much better and that conveys the conclusion you made above

That’s it. Here is the before and after:

The conclusion – “AI adoption vastly lags its theoretical capability” – is now the title. Categories are sorted, the gap is immediately visible, and there is nothing left to decode.

See the standalone version of the graph (the raw data, as extracted by Claude, is in that file).

For more on why radar charts are almost always a bad choice, see my earlier posts on spider charts and radar charts.

But, Boris, vibe plotting is not a real expertise!

Sure, but Anthropic people have access to the same vibe plotting tool I have (even better). It’s knowing what to ask for that is the real expertise. And if you don’t have that, you can always ask someone who does.

I have put together a collection of lecture, workshop, and course proposals on AI and AI-assisted programming. Each one is tailored to a specific audience and comes in three delivery formats: a one-hour lecture, a three-hour hands-on workshop, and a multi-session course.

The topics range from a non-technical introduction to AI (for students from any department) to hands-on AI-assisted programming for CS students, CS lecturers, industrial engineering students, managers, and life science researchers. Most proposals are available in both English and Hebrew.

All proposals are now collected on a single Lecture & Workshop Proposals page.

Anthropic has published an interesting study about labor market impacts of AI: Labor market impacts of AI: A new measure and early evidence.

It is hard to overestimate Anthropic’s contribution to the world of technology. Their innovation and professionalism are remarkable. But sadly, in the report they included a spider graph that leaves their entire post with a bad taste. (I wrote about spider charts many times before.)

It takes a good minute or two to understand what the image is about. Spider charts are almost always a bad data visualization technique. So how do we fix it?

First, we need to define the conclusion. A short sentence of up to 8 words that conveys the general idea of the graph. After that, we create a graph that does exactly that – conveys the conclusion.

Here, the conclusion is the difference between theoretical AI capability and the observed AI adoption across occupational categories. So we need to show exactly that difference.

Look at the improved graph. All the categories are sorted according to the observed adoption rate. The differences are immediately visible, and the conclusion is right at the top of the graph, leaving no room for guessing.