I asked him for feedback. He was honest. Six weeks is an unfair window to judge anyone, he said, and he knew it. But he had to let someone go, and from the way I had communicated, he never got the impression I was someone the team could not replace.

Here is the part I still think about. Since 2016 I had been teaching workplace communication, in a college and as a freelancer. I taught people, for money, how to make their value legible at work. And I got cut because I had not made mine legible. The communication teacher, cut for communication.

Why is your worst blind spot the thing you are best at?

Because expertise quietly turns into an assumption. You are good at the thing, so you assume the thing is handled, so you stop looking at it. The looking is exactly what you sell to everyone else, and it is the one place you never point it.

There is an old line for this. In the Gospel of Matthew: “Why do you look at the speck in your brother’s eye, but do not notice the beam in your own?” (Matthew 7:3). The Talmud puts it more sharply, because it makes the beam the expert’s problem. טול קורה מבין עיניך, “take the beam from between your own eyes” (Bava Batra 15b). The person quick to point out your speck is usually walking around with a plank in his.

I would like to tell you I learned this once, in that HR office, and fixed it. I did not. Years later I built a service whose entire purpose is to make researchers’ work visible, to get good work in front of the people who should see it. And for months the service itself had no visible presence. No blog. Nothing a person, or an AI search engine, would surface when they went looking. I had spent years telling people to take the speck out of their eye, and walked around with the same beam in mine.

How do you find a blind spot you cannot see?

If you could see it, it would not be a blind spot. So you cannot wait to notice. You have to go looking on purpose, in the place you least expect to find anything, which is your area of competence.

Three things have worked for me, none of them comfortable.

Run your own audit on yourself. You already have a checklist you apply to other people’s work in your field. Apply it to your own, coldly, as if it belonged to a stranger. The communication teacher never once graded his own first six weeks.

Ask for the feedback before someone is forced to give it to you. The only honest feedback I got at that job arrived in the room where it was already too late to use it. Ask the question while you can still act on the answer.

Put your own work on the record, the way you tell everyone else to. Not because the world is waiting for it, but because making it legible is how you find out whether you have been doing the work or just assuming you have.

Loud Camel news

This week I finally pulled the beam out of my own eye a little. Loud Camel, a tool that helps researchers get cited and recognized, is the service that had no visible presence of its own, so I stood up its blog, moved nine posts onto it, and turned on analytics so I can see who shows up instead of guessing. None of that is a launch. It is just me finally doing for my own service the thing I keep telling everyone else to do.

Takeaway

Point your expertise at yourself this week, in the one area you are most sure you have handled. That is exactly where the beam is.

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.

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.

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.

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.

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.

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.

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.

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.

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.