The man who studied delayed recognition, and then suffered it

Eugene Garfield is the reason we count citations at all. He built the Science Citation Index, he gave us the impact factor, and in 1980 he wrote a short piece in Current Contents with a plain title: “Premature discovery or delayed recognition-why?” In it he named a thing every researcher half-suspects is real. Some papers sit almost uncited for years, then wake up and get cited a lot, long after most of the people who could have used them have moved on. Anthony van Raan later borrowed a nicer name for these papers from Perrault and the Brothers Grimm: sleeping beauties.

Here is the joke. Garfield’s own paper on delayed recognition was itself delayed-recognized.

That’s the finding of a 2025 preprint by Tariq Ahmad Mir and Marcel Ausloos, Forsaking your own (arXiv:2512.16943). It is a preprint, so treat it as a strong claim, not a settled one. But the claim is clean, and I’ve been chewing on it for a week.

The numbers

Garfield published the paper in 1980. By 2004 it had collected about 10 citations. That is 0.4 citations a year for a quarter century, from the single most cited name in the field that invented citation counting. For 23 of those years it never once cracked more than one citation in a year. The authors call it a deep sleep of 28 years.

Then it woke. Counting up to 15 September 2025, they find 45 citations in Scopus, 93 in Web of Science, and 205 in Google Scholar, which they reconcile to 214 unique citing papers (234 if you count Garfield citing himself). A paper that averaged one citation every two and a half years for decades is now a normal, respectably cited paper. On the beauty coefficient, a standard sleeping-beauty score, it lands at 159.55 and 144.62 across two citation peaks, in 2018 and 2023. Whatever threshold you pick, it qualifies.

Someone has to be the prince

What woke it up? Van Raan’s 2004 paper, the one that rechristened these papers “sleeping beauties.” After 2004 Garfield’s paper gets cited far more, and Mir and Ausloos note that in 141 of the 187 citations it collected after van Raan, roughly three quarters, it is co-cited with van Raan. The old paper rode in on the coattails of the new one.

There is an uncomfortable lesson here, and it is not about Garfield. Van Raan does not even cite Garfield’s 1980 paper. The revival happened anyway, because a well-placed, well-cited paper made the topic legible again and people went looking for the roots. Garfield’s paper needed a prince. It got one by luck, not because the work finally spoke for itself.

That’s the part I keep turning over. We tell ourselves that good work eventually gets found on merit. This is a case study in the opposite. The idea was correct. The author was famous. The paper still sat there for 28 years, and what saved it was somebody else’s visibility, not its own quality.

Where the story gets shakier

I run a company whose whole premise is that good research gets ignored and that being right isn’t enough. So I’m the last person you should trust to be skeptical here, and I want to be anyway.

“Delayed recognition” is not a fact of nature. It is a definition, and the definitions are somewhat arbitrary. Garfield’s own rule of thumb was 10 or fewer citations at age 10 and a tenfold jump by age 20. Van Raan added his own thresholds for the depth and length of the sleep. The beauty coefficient is yet another formula. Move the cutoffs and some sleeping beauties stop being beauties.

The authors are honest about a messier problem too. Garfield published in Current Contents, which was not peer reviewed, so Web of Science and Scopus record its metadata badly. Web of Science at one point piled the citations of 52 different articles from one 1980 issue onto a single paper. Every count above came from manual cleanup across three databases. And, again, it is a preprint. None of that kills the story. It just means the exact numbers matter less than the shape, and the shape is not in doubt.

If your work is being ignored right now

Read this as comfort or as a warning. I mean both.

The comfort: silence is not a verdict. Garfield’s paper was not wrong for 28 years and then suddenly right. It was the same paper the whole time. If your work is being ignored, that’s information about attention, not about quality.

The warning: attention does not show up on its own, and it did not show up for Garfield either. His paper waited a quarter century for an accident. Most papers never get a famous prince to co-cite them into daylight, and most of us don’t have Garfield’s name on the byline. If nobody knows the work exists, its merit never gets a turn.

So the question the paper leaves me with isn’t whether your good work will be recognized. It’s who, exactly, you’re counting on to be your prince, and whether it’s wise to wait for one to wander by.

Is your hot streak also your most disruptive streak?

There is a flattering idea floating around science-of-science, and it goes like this: your best work doesn’t dribble out evenly across a career. It arrives in a burst. A few golden years when almost everything you touch lands, and around them, before and after, the ordinary stuff.

That burst has a name. Liu et al. called it a “hot streak” in 2018: a bounded stretch in a creative career during which a person produces their highest-impact work. The unsettling part of their result was that the streak seems to strike at a random time. You cannot schedule it. You get one, if you get one at all. I find this idea seductive, and I distrust things I find seductive, so a new paper caught my attention.

What the study found

Hongkan Chen, Lutz Bornmann, and Yi Bu asked a narrow, testable question. Not “when does your best work happen,” but “when your best work happens, is it also your boldest?” (Humanities and Social Sciences Communications, 2025) Their answer, from the careers of 21,271 economists: yes. “Disruptiveness of scientific publications is higher during researchers’ hot streaks than in their other periods of careers.” During the streak you’re not only cited more. You are also more likely to publish work that shoves the field in a new direction instead of reinforcing the old one.

What “disruptive” means here

That word needs a definition, because it is doing a lot of work. Some papers consolidate what we already know; later work leans on them and on their predecessors together. Other papers disrupt: later work cites the new paper and quietly stops citing what came before, as if the new paper made the old scaffolding unnecessary. The disruption index puts a number on that difference. A high score means your paper eclipsed its own references. The claim here is that hot-streak papers score higher on it.

Two findings I did not expect

First, the boldness is random too. Just as hot streaks land at unpredictable times, so does disruptive work: “we also observe the randomness rule for the occurrence of disruptive work.” You do not graduate into disruptiveness at a fixed career age.

Second, and this is the one I keep chewing on: “researchers’ most disruptive years are oftentimes earlier than their most highly cited years.” Your boldest paper and your most-cited paper are usually not the same paper, and the bold one tends to come first. The recognition lags the risk, sometimes by years.

What I don’t believe yet

I like this paper, and I don’t fully believe it yet.

It is economists. 21,271 of them is a large sample, but it is one field with its own citation culture. I wouldn’t bet the pattern transfers unchanged to molecular biology or to poetry.

The disruption index is contested. People argue about what it actually measures and how much it wobbles with database coverage and reference counts. Read “more disruptive” as “scored higher on one specific metric,” not as a verdict handed down by nature.

It is a correlation. Hot streaks and disruption travel together in this data. The paper doesn’t, and can’t, tell you that being disruptive triggers the streak, or that the streak makes you disruptive, or that some third thing drives both.

And one lever in the paper is not random at all: volume. “A higher publication volume of researchers increases the likelihood of entering hot streaks and producing disruptive publications.” Publish more, and you raise your odds of both. That is the least glamorous sentence in the paper and possibly the most useful.

Why care, if you are watching your own trajectory

If you’re staring at your own CV and wondering whether your good years are behind you or still ahead, this paper offers a small, cold comfort. The good years don’t seem to be something you earn on a schedule and then lose. They arrive when they arrive. What you can do is keep the tap open, because output feeds the odds. And if you’re sitting on a paper that feels riskier than your usual, the timing result hints that you might be earlier in a streak than the citation counts will admit for another few years.

The authors put the practical conclusion plainly, aimed at funders as much as scientists: “both scientists and funding agencies can assume the randomness of phases with important papers in scientific careers. But when these phases occur, impactful and disruptive papers can be expected.”

I’m glad they stopped there and didn’t try to sell me a recipe for manufacturing a hot streak. Because here’s what still nags at me. If the streak is random, and the disruption is random, and the two merely happen to move together, then the comforting story might collapse into something much plainer: sometimes a scientist has a good run, and during a good run good things cluster. Is that a law of creative careers, or is it just what any run of luck looks like from the inside? I’m not sure the data can tell those two apart. Are you?

Small teams disrupt? Maybe it was never about size

You’ve heard the line even if you never opened the paper. Small teams disrupt, large teams develop. It comes from Wu, Wang and Evans in Nature in 2019, and since then it has been quoted in grant panels and on conference stages every time someone argues that science has grown too big to be brave. Small and scrappy breaks new ground. Big and well funded refines it.

A new preprint says the size part was mostly standing in for something else. Bili Zheng and Jianhua Hou, in “Synergy, not size”, argue that what drives disruptive work is not how many people sign a paper but how the collaboration is wired. It went up on arXiv in September 2025 and has not been peer reviewed, so read it as a strong claim, not a settled result.

What Zheng and Hou actually did

They looked at more than 14 million papers across 19 disciplines from 1960 to 2020. Instead of counting heads, they built what they call a synergy factor, a way to score the cost and benefit of adding people to a team. Then they ran a mediation analysis, which is a formal way of asking a simple question: when team composition predicts disruption, is size doing the work, or is something that size is merely correlated with doing the work? Their answer is that synergy, not team size alone, accounts for 75 percent of the link between who is on the team and how disruptive the paper turns out to be.

The best team size is not one number

The result that stuck with me is that there’s no single best size. Physics peaks at medium sized teams. The humanities reach their highest synergy through individual scholarship, one author alone. So “small teams disrupt” really means that the right architecture depends on the field, and in some fields the right architecture is a single person.

Star authors help, but not the ones you would rank highest

Two more numbers. Papers that include an exceptional researcher show, by their measure, 561 percent higher disruption. And a twist that should make anyone who reads CVs squirm: high-citation authors were linked to less disruptive potential, while authors with a track record of breakthroughs were linked to more. Being cited a lot and doing something new are not the same signal. Zheng and Hou sort teams into four modes: elite-driven, baseline, heterogeneity-driven, and low-cost.

So was “small teams disrupt” wrong?

Not really. This is not a refutation. It is a rewrite of the caption. Size was never the cause. It was a visible proxy for how a team combines skills and viewpoints. Small teams disrupt more, on average, because small teams are often wired for synergy by default. Build a large team that keeps that wiring and, in principle, you keep the disruption. That is a more useful claim than “stay small,” because wiring is something you can actually decide.

What I would not bank on yet

Three caveats, in order of how much they bother me. First, it is a preprint. No peer review, no independent replication that I’ve seen, and a 561 percent effect is exactly the kind of number a reviewer tugs on to see what falls off. Second, it is observational. Fourteen million papers is a mountain of correlation and zero experiments. Nobody randomly assigned scientists to teams. Third, and this is the one I keep circling back to, “disruption” here is a citation-pattern score, not a verdict on importance. The disruption index asks whether later work cites you instead of the things you built on. That captures something real, it also misses plenty, and the index has its own critics. When the paper calls a team disruptive, it means a shape in the citation graph, not a Nobel.

If you are choosing who to work with

Here is why a working researcher should care. If you’re picking co-authors, the size heuristic is easy and mostly wrong. This preprint’s version is harder and more honest: ask what each person adds that the others cannot, and whether adding them raises synergy or just raises the author count. A collaborator with one real breakthrough behind them may matter more than three with fat citation totals. And if you work in a field where solo work still disrupts, the reflex to bolt on co-authors may be quietly costing you the exact thing you were trying to buy.

I would hold all of it loosely until it clears review. But it does reframe a line I keep hearing repeated as if it were settled physics. Maybe the question was never how many people are in the room. Maybe it was always what happens once they are.

Is science really running out of disruption?

You probably saw the headline in early 2023. “Papers and patents are becoming less disruptive over time,” a study in Nature announced, and the internet did what the internet does. Science is running out of ideas. The age of breakthroughs is behind us. We are all filing footnotes now. Behind the headline sat one number: the CD index, also called the “disruption index.” It scores every paper on a scale from -1 (fully consolidating, it builds on what came before) to +1 (fully disruptive, it makes earlier work obsolete). Michael Park, Erin Leahey and Russell Funk, PLF for short, found the score had been sliding downward across every major field for decades, and concluded that progress was slowing.

A new paper in Research Policy (Newig et al., 2026) says: not so fast. Jens Newig and thirteen co-authors reassess the whole framing, and their message is blunt. In the social sciences, a high disruption score usually does not mean a breakthrough. It measures something closer to noise: relabeled ideas, missing citations, subfields talking past each other. Progress there is cumulative anyway, so using disruptiveness to judge whether research matters, or to declare that science is stalling, is measuring the wrong thing. This is a conceptual paper, not a reanalysis. They ran no new data (the paper says so outright). What they did was take PLF apart argument by argument, drawing on the philosophy and sociology of science, and hand the field a set of testable hypotheses.

How does a paper score as “disruptive”?

The mechanism is simpler than it sounds, and that is the problem. The CD index looks at the papers that later cite yours, and asks whether they also cite the works you cited. If they keep citing your sources alongside you, you look consolidating. If they cite you and drop your references, you look disruptive, as though you rendered everything before you obsolete. Nothing in that arithmetic knows why the later citations skipped your references. Newig et al. list four kinds of papers that score as disruptive without disrupting anything:

• Pseudo-novelty. Old wine in new bottles: relabeling an existing idea with fresh terminology. Their own Scopus search turned up more than 4,900 papers with the phrase “fresh look” in the title, abstract or keywords, 27% of them in the social sciences and 21% in arts and humanities, even though those fields are only 8% and 4% of Scopus.
• Blockbuster and canonical papers. Citations that are ceremonial, name-dropping a famous work “to shine in their reflected glory” rather than depending on it.
• Citation gaps. Sloppy or strategic omission of prior work, which fakes the look of having displaced it.
• Purely cumulative papers. A meta-analysis or systematic review synthesizes a field so well that later authors cite only it and skip the originals. That is the textbook shape of cumulative science, and the CD index reads it as disruption.

Each type may hit only a subset of papers, but together, the authors argue, they add up to a meaningful share of artificially inflated scores.

Why the social sciences look the most disruptive

Here is the number that carries their case. In PLF’s own data, social science papers had the highest CD values of the four fields the entire time, falling from about 0.54 in 1945 to 0.04 in 2010, while life and physical sciences sat at the low end. Newig et al. read that ranking the opposite way to PLF. The social sciences do not look disruptive because they break more ground. They look disruptive because they are fragmented. Richard Whitley’s phrase for it is “fragmented adhocracy”: research that is “personal, idiosyncratic, and only weakly coordinated across research sites.” Watts (2017) puts it more bluntly, that in such fields “facts and theories pile up in an incoherent heap.” For a real Kuhnian disruption you first need a paradigm to disrupt. Where there is no shared consensus to overturn, what looks like disruption is what they call pseudo-disruption: an academic “turn,” a fashion, an outside influence, not a genuine break.

The trend was already shaky

And “disruption is declining” was contested before this paper landed. Independent reanalyses had pulled at it. Petersen and colleagues (2024) argue the decline is largely an artifact of citation inflation: reference lists have grown longer over the decades, which mechanically drags CD scores down. Others trace the patent version of the decline to the omission of older references in PLF’s own dataset. A separate line of critique (Leibel and Bornmann, 2024) notes that the index is sensitive to how many references a paper has and how well cited they are, and that social science papers, which cite books that citation databases like Web of Science do not index, get artificially inflated CD values because those book references are invisible to the machinery. So there were already technical reasons to doubt the trend.

What Newig et al. add sits one level up. Even computed perfectly, the index may not mean what evaluators want it to mean. A measure is only useful if it maps to the thing you care about. If the thing you care about is “did this work advance the field,” the CD index answers a different question and hands you a confident-looking number regardless. They push further than most of PLF’s critics: genuine, substantive disruption, the kind that truly renders earlier findings obsolete, may be rarer than the metric suggests, not more common.

Fragmentation, not disruption, is the real opposite of cumulation

They do not only poke holes. They offer a different map. Disruption and cumulation, they argue, are not two ends of one road. The real opposite of cumulation is fragmentation. So they draw two axes, disruptive-versus-consolidating and cumulative-versus-fragmented, and most good work lands in the cumulative-and-consolidating corner, which is just Kuhn’s “normal science”: replication, refinement, synthesis. A recent survey of 761 major breakthroughs (Krauss, 2024) found that virtually all of them developed cumulatively rather than by rupture, which fits the picture. Disruption earns its keep only when it does real work, mainly falsification: a failed replication that kills a wrong result, which feeds the cumulative pile rather than blowing it up. The classic case the authors borrow from PLF is Watson and Crick’s DNA model refuting Pauling’s triple helix. A new “turn” that merely changes the subject is not progress. It is, in their framing, fragmentation wearing novelty’s clothes.

Why this matters if a metric is scoring you

If you’re early or mid-career, this isn’t abstract. Disruption-style metrics are drifting into hiring talks, grant panels, and the dashboards that try to score a person. In a cumulative field, the paper that carefully extends three others is doing exactly what progress looks like, yet on a disruption index it scores low, while a disconnected, thinly-referenced outlier scores high. Optimize for the number and you would be nudged to cite less and to pretend your work sprang from nowhere. The authors say the quiet part directly: research policy and evaluators should not treat high disruptiveness as inherently valuable, or low disruptiveness as stagnation, and PLF’s declining-disruption conclusion “should be interpreted with care.” You do not have to win that argument in the room. You just have to be able to name it.

The limits are the authors’ own. They did not prove the four mechanisms dominate the data. They hypothesized them and invited the rest of us to test them empirically. Their case against PLF is an argument, a good one, not a verdict. And none of it proves science is fine, or that stagnation is a myth. Real slowdown might be happening. The narrower claim is that one popular number is a poor way to check, especially in the messy, unconsolidated fields where it happens to score highest. So before you let a disruption score speak for your work, or anyone’s, it’s worth asking the old question. Says who, and measuring what?

This autumn Israelis vote at the end of October, and Palestinians vote thirty-one days later. It will be the first Palestinian election in twenty years. So I invited Samer Sinijlawi back to the podcast for the third time.

Samer is a Fatah member, a Palestinian political activist, and chairman of the Jerusalem Development Fund. The first time we recorded, in March 2025, his claim was that peace is more urgent for Palestinians than for Israelis. The second time, in November 2025, he said that what October 2023 exposed was not a lack of information but a lack of imagination. This time he came with something new: he is building a party.

His thesis is one sentence long. For sixty years Palestinians tried to pressure Israel, with violence on the ground and with diplomacy abroad, and it produced nothing. The only road to a Palestinian state runs through convincing Israelis, which means putting Israel’s security at the base of Palestinian national strategy. Not as a favor to us. As Palestinian self-interest.

I should say where I stand. I am the pessimist in this conversation. Near the end I told Samer that I am for the vision he describes but that I find it hard to see it happening, because nothing good has happened here for a long time, and because building trust is a matter of years. I do not know whether he is right. I do know that talking to him leaves me less bleak than I was before, which is not the same as being convinced.

The full conversation, in Hebrew, is on YouTube: https://youtu.be/XzPBGczt4vY. What follows is translated from the Hebrew and edited lightly for clarity.

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Why go to Washington, ten thousand kilometers away, or to Paris, four thousand kilometers away, when we have the Israelis five kilometers away?

Twenty years without an election

Boris Gorelik: Before we get to your party, give us a picture of how Palestinian politics actually works. The legislative council, the geographic split between Gaza and the West Bank, and what people are voting on.

Samer Sinijlawi: The Palestinian political system is closer to the French one. There is a parliament, a government, and a president, so there are two elections. There is the presidential election, and there is the election for the legislative council, which has the right to form a government and run Palestinian affairs.

Our last presidential election was in 2005. Mahmoud Abbas was elected as Fatah’s candidate, after Yasser Arafat.

Boris: Who did he run against?

Samer: Against Mustafa Barghouti, an activist from the left, if I can call it left. Abbas won 60 percent of the vote, for a four-year term. And then he cancelled the elections and continued for twenty-one years.

In parallel, in 2006, we had the election for the legislative council, our parliament, which also forms our government. Hamas won there, about 60 percent of the seats, against 40 percent for Fatah. In 2007 came the coup in Gaza, when Hamas took the territory by force. From then on we had two rulers: Hamas ruling Gaza, and Abbas ruling the West Bank. And no elections.

In 2018 Abbas dissolved the legislative council and started to be the legislating body himself. Every Palestinian law since then has come through a presidential decree. The president decides which law is enacted, what is legislated and what is not. All the power in the hands of the president. That is the West Bank. In Gaza, Hamas ruled 100 percent.

Today, after all these years, after a lot of pressure from us and also pressure from international actors who wanted a change in Palestinian politics and in the Palestinian leadership, we finally have a date. It is thirty-one days after the election on the Israeli side.

Can Gaza vote? Can Jerusalem?

Boris: Would the elections take place in the West Bank and in Gaza at the same time? And what happens with the residents of East Jerusalem?

Samer: Simultaneously, yes, that is the plan. As for Jerusalem, under the Oslo accords Palestinians there are allowed to vote. Israel has to approve that we use the Israeli post offices, people vote there, and afterwards the boxes go by mail to the Palestinian Central Elections Committee. That is how it worked in 1996, in 2005, in 2006, in every election held in the Palestinian Authority. But the recent Israeli governments have not approved the participation of Palestinians inside Jerusalem.

There is a solution. The Central Elections Committee has electronic voting, and I think we should go in that direction.

Gaza is a different set of challenges. Gaza today is not the Gaza we knew. There is almost no Gaza. There are three large populations in three different places: in the middle of Gaza City, in the center around Deir al-Balah, and in the al-Mawasi area. So maybe the committee needs to set up three election hubs, each one tied to where a large population is. There is a technical solution for running this election.

Boris: And Hamas in Gaza would allow it?

Samer: I think yes. They allowed the municipal elections to take place in Gaza in the past. And you should know that Hamas wants out of governing Gaza. It does not interest them to keep being the government there.

Why? Because they did not only lose power, they also went bankrupt. They are not paying salaries to their civil servants. They owe something like a billion dollars in debts to local banks. They simply went bankrupt. They know they cannot run this event.

There is some negotiation going on now with the peace envoy and his representatives, and the American envoys. On one side Hamas says: we can hand over the entire government right now to the national committee for administering Gaza, and we are ready to hand over the police and the security apparatus of the government, everything we received from the Authority in 2007, including the weapons of the local police. And after that succeeds, let them start disarmament, we will give them the tools to do the work. On the other side, the Israelis and the Americans say no, disarmament first. And that is where everything gets stuck.

We are also stuck in Gaza because it is an election season on the Israeli side. I do not think the political echelon wants any progress in Gaza right now. Let us see after the elections.

“I do not believe the polls”

Boris: You say Palestinians are fed up with Hamas and with Abbas. But if I am not mistaken, the pollster Dr. Khalil Shikaki shows support for Hamas, and in the West Bank even more than in Gaza.

Samer: Listen, I also follow the polls on the Israeli side. If I look at Channel 14’s polls [Channel 14 is the strongly right-wing, pro-Netanyahu channel], that gives you one picture. If I look at Channel 11, or 12, or Haaretz [11 is the public broadcaster and 12 the main commercial channel, both roughly centrist; Haaretz is the strongly left-wing newspaper], it depends who runs the poll. I do not believe these polls. I say it is a black box. You have to go to real elections, and there we will find out who has what power on the Palestinian side, whether Palestinians want change now or whether Palestinians are stuck where they used to be. And especially now, with everything going on in Gaza, I do not think you can run a poll accurately.

But I know Palestinian society a little. I say “a little” because I want to be modest. There is the Fatah bloc, Abbas’s bloc. There are 60,000 Palestinians inside the Palestinian security apparatus. There are 250,000 people employed by the Authority. That gives a lot of influence. So Abbas will still have power, maybe 25 percent. And Hamas will stay 25 percent.

Boris: Would Hamas run as a party under its own name?

Samer: They will find a solution. They will do some rebranding, they will come in through lists that look independent, and everybody will know what it is. Understand that Hamas is a political movement that has a military wing, not the other way around. For them, not participating in national elections is suicide. There is no such thing. They have to be there. They will try to build coalitions with others. I do not know what their final list will look like, but I am almost certain they will run in this election.

So there is 45 or 50 percent that is the Abbas and Hamas bloc, and there is 50 percent, maybe more, maybe 60, of voters who want something new. And there are new centers of power now presenting themselves as players in the coming election. There is Mohammed Dahlan and his stream, who have organized as a party or a list, and I think they will be a significant force. There is the New Way, the party we are building. There may be others who put up lists.

Palestinians are fed up with Hamas and with Abbas. Fed up with the destruction Hamas brought down on them in Gaza, and fed up with the corruption Abbas brought down on them in the West Bank.

Boris: If I remember correctly, the commentators back in 2006 said the public was fed up with Fatah’s corruption, and that Hamas was elected not because the public is so religious but because it did not want the Authority’s crooks. If that is true, then the chance for a party like the one you are building is high, because Fatah has not become less corrupt, and certainly Hamas has not.

Samer: Correct, although the picture is different now. In 2006 the Palestinian public had Fatah’s failed product in front of it. They saw corruption and they saw no progress. Oslo said five years of an interim agreement and after that we go to a permanent status arrangement. From 1993 to 1998 there was no progress, and we arrived at the Second Intifada. So Fatah’s product was not very convincing.

There was disappointment, and Hamas was the alternative. They also used the fact that they were victims of both sides, of the Palestinian side and of the Israeli side that fought them very hard, and they took a place in the heart and the mind of the Palestinian public. And they were smart, they picked candidates who were connected to the public. Let us say everything was working in their direction. So they rose.

Now the Palestinian public knows where Hamas led us. Gaza was erased. I do not think there will be another vote for Hamas or for Abbas. But they still hold power in the street.

What Palestinians are actually looking for

Samer: Palestinians today, Boris, get up every morning knowing that today is worse than yesterday. If there is a political force that can carry them and give them not only a vision but a practical vision, a plan that tells them there is a way for life to get a little better every morning, that answer matters. Palestinians are looking for that answer. That is where our opportunity is.

I see that inside Gaza in particular there is a desire for change. From my own activity I see how many Gazans are watching and hoping that someone can bring a force that can put things in a better order.

I have to put the security of the Israeli side at the base of every Palestinian national strategy. Not as a favor to you. To serve the national interest of my own people.

Is there a partner?

Boris: In our first conversation, in March 2025, your claim was that peace is more urgent for Palestinians than for Israelis, precisely because their situation keeps getting worse. Let me stay on the realistic side. Not that we all suddenly sit down together and start arguing about whose hummus is better. Do you still see a situation in which Israelis and Palestinians elect governments that are capable of working with each other, that do not maintain the conflict but find a way for us to say, twenty-five years from now, that the conflict has ended?

Samer: Look, the partner problem has been a serious problem in recent years. Israelis really did look for partners and did not find them on the Palestinian side. And part of the Palestinian people also lost hope that we have a partner on the Israeli side.

Everyone who says there is no partner on the other side and there never will be a partner on the other side is wrong. There is no partner now. I think there will be a partner. I am optimistic. I see a change that is almost arriving on the Israeli side. I can already imagine the next Israeli cabinet. With that next Israeli cabinet I think yes, there is a partner, not only for Palestinians, there is a partner for the whole world, and that is something Israel badly needs.

Israel has disconnected, and not only on the Palestinian question. You are also moving far away from your serious friends around the world, including Europe, including the moderate Arab states. In the last three years I have not heard of an Israeli prime minister receiving an invitation to some capital in Europe, or some capital in the Middle East. Even now such meetings are refused, in the United States too. So there is an Israeli need, an internal one, and there is an international Israeli need, for Israel to produce a different political leadership that can repair all the damage in the international theater.

A change like that also gives us, as Palestinians, options. We can look at the Israeli side and say we have an opportunity now, there is someone to talk to. But if that change on the Israeli side is not matched by a parallel change on the Palestinian side, it stays an Israeli event. The moment there is change on the Palestinian side too, it starts being a regional event, an international event, and it starts to be interesting to everybody.

The New Way: criticize ourselves, not them

Samer: That is the new thing we are bringing into Palestinian politics now. The New Way. We say the way has to change.

We focus more on where we went wrong, not on where the other side went wrong. Look at this conflict. Both sides always want to give lectures to the other side. You are wrong about this, you need to do that. But there is never internal criticism. Here we concentrate on internal criticism. We do not want to discuss their mistakes. Let them find their own mistakes, and what they fix is their business. Here we look at the mistakes we made.

For sixty years we always tried to pressure Israel by using violence on the ground and by trying to recruit the whole diplomatic world against Israel. That strategy failed. We got nothing from it.

We believe the only way that can bring good outcomes for the Palestinian side is to try to convince the Israelis, not to pressure them. Why do we need to talk to the entire world about Israelis? To go to Washington, ten thousand kilometers away, or to Paris, four thousand kilometers away, when we have the Israelis five kilometers away? So why not talk to the Israelis. It is easier for us to convince the Israelis than to convince the whole world to pressure the Israelis.

“You are strong. Do not rely on anyone.”

Boris: Especially after 7 October, people tell me: fine, Samer says nice words, but all they want is to lull us to sleep. Maybe they are right, maybe not. But that is an experiment I do not want to run on my own life again.

Samer: You do not need to run it on your life. Do not rely on anyone, Boris. You are a strong country. And make no mistake, even after 7 October you have the strongest army in the Middle East. 7 October did not happen because the Israeli army is weak. It happened because there was no Israeli army on the Gaza border that morning.

You are strong. You do not need to rely on anyone. Take care of your own security yourselves. Do not rely on a partner on the Palestinian side and do not rely on a partner on the American side.

But that does not mean you come in this direction from a position of weakness. The opposite. You demonstrated the strength you managed to demonstrate in front of the whole world, and you come to this from a position of strength. And any strength that has no diplomatic option next to it is worth nothing. The military option can buy quiet. It cannot buy peace and strategic security forever.

Israel’s security will be created only through diplomatic effort. A regional order that brings in all the new players, including the Saudis. Israel starting to be part of the Middle East, connected to everyone. And a Palestinian government that believes the basis of any successful Palestinian national strategy is the security of the Israeli side.

I have to put the security of the Israeli side at the base of every Palestinian national strategy. Not as a favor to you. To serve myself, to serve the national interest of my own people.

“We are both looking at each other from the surface”

Samer: Right now the Israeli public perceives us very badly. Why? Because the two of us look at each other from the surface, and the picture on the surface is a black picture. It is not a pretty picture. We both did bad things to each other. There is no Mother Teresa here.

But if you start looking a little deeper, we are human beings and you are human beings. Even if we are perceived by you as if everything we want is to destroy the State of Israel and not to build a Palestinian state, that is not accurate. I know both peoples well, and I say these things inside Palestinian conversations too: Israel does not want to destroy us, it does not want to throw us into the sea. Do not listen to Smotrich, do not listen to the extremists, they do not represent the Israeli public. The Israeli public wants to live in peace, in quiet, in security. If they know that a Palestinian state will supply them with security, they are ready to leave the territories and allow it to happen.

But they are not there. We have to convince them that a regional order that brings a Palestinian state next to the State of Israel can supply Israel with security. And to get there, we have to change many things.

We are not important at all. You, the Jews, are important. We were lucky that we fell into a conflict with you.

Why Egypt was the easy case

Boris: How many years, in your estimate? Not counting miracle governments, but realistic ones that move the issue forward. How many years until this is not as bad as it is now?

Samer: Let us look at a piece of history. The October of the 1970s was maybe similar to what is happening today. You had thousands of Israelis killed in October 1973. There was strong hatred in the Israeli people toward the Egyptians, and from the Egyptians toward the Israelis, and nobody thought it was possible to make peace with Egypt. And suddenly the Egyptian leader took one step, went up to the Knesset podium, and in less than an hour it changed.

But I will tell you the biggest difference. Egypt and Israel are very far from each other. So the leaders, Begin and Sadat, could sign a beautiful agreement with Carter, and the armies could put away their weapons. Here we are really close to each other. I think that today an Israeli walking into Cairo, a Jewish Israeli walking into Cairo, is better off not saying that he is Israeli and Jewish.

Here we are much too close for that model to work. Say Mohammed Dahlan and the next prime minister of Israel get up and sign some agreement. There will be balloons, they will fly. I still do not think we would be able to live in peace.

Boris: So it is not only a matter of a signature. It is a matter of deep internal change.

Samer: If a Palestinian political force rises and gets support from the street, and says we start from the base, we change the Palestinian narrative.

Rewriting the Palestinian narrative

Samer: We recognize the historical connection, the historical right of the Jewish people in this land. That is a fact, and it is a fact that is in the Qur’an. Why do we run away from it? Why do we not recognize this right? They were here. They belong here. You, the Jews, were here, and you belong here.

But look at the facts from your side as well. You were never alone here. There were always others, and the others are us. So nobody keeps rolling this conflict under the headline of either us or them. Both of us together.

And after that recognition, that same political force says: zero violence. If we want to produce good outcomes for both of us, we have to talk to them and convince them, and we will not allow violence. And there will be support from the street strong enough that all Palestinians will believe we have to resist violence, not to let violence take the lead. And then we start talking, and we tell you: both of us belong here.

We had coexistence here for thousands of years. We got tangled up in the last hundred. Let us find the shared future together, two peoples next to each other, cooperating on everything.

“We are lucky that we fell into a conflict with you”

Samer: I always ask a question of my Palestinian friends, in all the meetings we hold now for the New Way. Why are we important? Why are we, the Palestinians, important everywhere in the world?

Most of the time they do not give me the right answer. The right answer is that we are not important at all. You, the Jews, are important. We were lucky that we fell into a conflict with you. That is not a joke. It is true.

And then I start telling them: imagine now what happens if we can change this relationship from a conflict into cooperation. If we become partners and not enemies, we gain a thousand times over.

(At this point one of Samer’s children walks into the frame.)

Boris: There is a partner for you. Number nine in the family. Say hi.

Samer: I have a big family. Nine children.

The settlers are much more connected to this land and to the people than people who sit and drink espresso in cafés in Tel Aviv.

“Please, defend the whole border”

Samer: You talked about why you should trust Palestinians on security. We need to trust you. I can imagine a Palestinian state next to the State of Israel that signs some kind of military protocol in which we ask the army of Israel to protect the borders of both states together.

Why do I need to buy weapons? I do not have the ability to buy. You have them. Where would I buy them from? I need to rely on you. Please, defend the whole border. I give you, with full willingness, the right to defend the entire territory together, under agreements between the two sides, so that you hold the security of all of us together.

If we can imagine things outside the box, we will find things that make you more open, because you no longer have the Palestinian challenge, which, as you said, we live with you. And if we get rid of that challenge, all the military and intelligence capacity of the State of Israel focuses on the real enemy of both of us, the one sitting in Tehran and in other places. We move out of the frame of an Israeli-Palestinian conflict into the frame of a conflict between the moderates and the extremists in the Middle East, with both of us on the side of the moderates, dealing with the challenges that come from the extreme sides.

It is possible. But first we need a leadership on our side that can reach the Israeli side, that can talk to the hearts and minds of Israelis, that can build trust, top down. Every Palestinian prime minister has to work hard to make the Israeli prime minister his best friend. Out of that kind of relationship, out of that friendship and that trust, we can build a system of trust between the two sides, in all the institutions of both sides, between the two peoples, and in the media of both sides.

What Israelis see, and what Palestinians see

Samer: I know it is hard for you now to look at the Palestinian side in a positive way. Two days ago I saw a report on Channel 11 about Gaza, by a correspondent who is also a friend, someone I know and respect. Ten, twelve minutes, and it only talked about Hamas in Gaza. But there are many other things in Gaza. There are families. There are people. There is a kind of hard life that the Israeli public also needs to know about, not only the security challenges.

And we, as Palestinians, must not go on looking at Israel only in a negative way. There are many positive things on the Israeli side that we have to explain to the Palestinian people through the media.

Where I did not expect the opening to come from

Boris: I get infected by your optimism. On the other hand, I am for the vision you are describing, but I find it hard to see it happening, because honestly nothing good has happened here for a long time. If it does happen, I will be very glad. But the vision you talk about, building trust, is a matter of years.

I read a book a while ago by Josef Avesar, an Israeli-born lawyer in California who works on divorce cases and who has a plan for solving the Israeli-Palestinian conflict, the Israeli Palestinian Confederation. His point is that when couples divorce, the real estate, which here is the territory, is usually the least important thing. There is far more insult, far more emotional damage, and his solution deals with all of that before it deals with the real estate. It is generations of work. But something has to be done, because the state we are in now is not good.

Samer: Certainly. Look, last week I had an experience. For the first time I met part of the leadership of the far right, let us say the Religious Zionism movement, people who live in the settlements. I was always afraid to talk to that part of Israel. I did not believe anything would come out of it.

Last week I found out that I was wrong. I think the chance that peace comes from there is bigger, because the settlers, the residents of Judea and Samaria, are much more connected to this land and to the people than people who sit and drink espresso in cafés in Tel Aviv. And there I found common ground. There I found shared people. There I found that religion does not push us apart, that religion can bring us closer, that there are many similar things on both sides. That is exactly where we have to focus, on the groups that could be spoilers. If we start talking there with an open heart and an open mind, we can move forward.

A smarter two-state solution

Samer: I talked about our shared right in this land. To be more precise, the historical connection of the Jewish people to this land was more in Judea and Samaria, in the West Bank. The Palestinian collective memory of this land is more the Gaza coast, Ashdod, Ashkelon, Haifa, Acre. The two-state solution gives you the coast and gives us Judea and Samaria.

But there is nothing to be done, this is demography. We are here, you are there. Any two-state solution has to be a smart solution that does not cut anybody off.

I sometimes ask myself why we should be demanding that the settlements be dismantled. Maybe we should be asking that they stay. Israel has 20 percent Arab citizens. The State of Palestine will keep 20 percent of the Jews who live there. But we are the state that serves their security. They live under Palestinian law, we respect them, we give them guarantees, we make sure everything is fine and in order for them, so that they live here, go to work in Tel Aviv in the morning, and come home to Hebron and to Beit El. Why do we need to dismantle the settlements? So maybe we should look for a smart model of two states. Anyone who does not feel like staying inside a Palestinian state and wants to leave, leaves voluntarily. Everyone who wants to stay in any case, stays.

We have to respect that symbolic connection, that poetic connection, that each one has to this land. And also respect that you need to live in your Jewish state, and we need to live in our national state, with dignity, and with very strong cooperation on everything.

The last word

Boris: If that happens I will certainly be glad, and I very much hope we go in that direction, because the direction you are drawing is really good. I want to live in a world like that, where we live together in peace and nobody is cutting off anybody’s hands, and then we can start really arguing about hummus and about food.

Until then, thank you very much. I think that two or three months after both election cycles I will bother you again and ask for your time, and we will analyze what happened and whether we are closer to your beautiful vision or actually further away from it.

Samer: Agreed.

---

The full episode, in Hebrew, is here: https://youtu.be/XzPBGczt4vY. The previous conversation with Samer is here.

Eight years ago my team lead posted a photo of me giving a talk in Barcelona, and a colleague reacted to it with a pile of poo.

I wrote about it at the time, in When “a pile of shit” is a compliment. The short version, for those who will not click. The talk was about the three most common mistakes in data visualization. The first mistake was about attitude and the third was about not writing conclusions, so I had an A and a C, and I wanted a B. The second mistake was about a low signal-to-noise ratio, and the best B word I could find for noise was “bullshit.” I was not sure I was allowed to put that on a slide, so I asked my colleagues in Slack. Four out of four said go ahead. Martin, who ran the data division at Automattic, added that for a non-native English speaking audience, American coinages like “bullshit” come across funnier and less aggressive than they do to some American ears.

So the slide said “Cut the bullshit,” half the data division had watched me agonize over it, and when the photo of that talk went up, the poo emoji was not an insult. It was a callback. It was affectionate.

The lesson I drew in 2018 was a human one: do not jump to conclusions, assume the best intentions.

I still believe that. There is now a reader in the thread who cannot do it.

The reader who was not in the room

Ask yourself what a language model would make of that thread if you handed it over today. Not the whole story. Just what is in the channel: a photo of a man presenting, and one pile-of-poo reaction. There is exactly one reading available, and a model will produce it fluently and with total confidence. Negative sentiment. Mild ridicule of the speaker.

It would not be malfunctioning. It would be doing exactly what I asked, with what I gave it. The joke lived in context the model never had, and here is the part that bothers me: unlike Sirin, or Martin, or anyone else in that channel, it has no way to notice that something is missing. A human who does not get a joke usually feels the gap. They ask, or they hedge, or they let it go. A model does not feel the gap. It fills it.

I published something a few days ago about my folder of markdown files, and I described the failure mode of an AI assistant like this: it “confidently does the wrong thing, because it guessed at something it should have asked you about.” Guessed. That is the same word I would use for the poo emoji, read cold. The 2018 story turns out to have been an early, funnier version of the thing I now spend my working day managing.

Context stopped being a courtesy

What changed between 2018 and now is not the technology. It is who is obliged to supply the missing piece.

In 2018 I could reasonably expect other people to supply it themselves. Everyone in that channel had watched the poll happen. If somebody had missed it, they could ask, or they could extend me the benefit of the doubt. That is what “assume the best intentions” actually asks of a reader: fill a gap you can see, using goodwill.

You cannot ask a model for goodwill. It has none, and it will not tell you it is short of anything. So the obligation moves. Context is no longer a courtesy I extend to a colleague who missed the meeting. It is an input I owe to a reader who was never at the meeting, never will be, and will answer anyway.

That is the actual reason my Claude setup is a folder of boring text files instead of a cleverer prompt. Those files are the poll. They are Martin’s note about non-native speakers. They are the thing that makes “Cut the bullshit” read as a joke about signal-to-noise instead of as a man swearing at strangers in Barcelona.

So write down the thing everybody knows

That is the whole technique, and it is much less satisfying than a clever prompt. Write down the thing you assume everyone knows, and put it where the machine can read it.

The test I use: if a competent stranger read only the artifact, and none of the conversation around it, what would they get wrong? Then go and write that into the artifact.

I might be over-reading my own emoji here. It is perfectly possible that the right answer is to keep the jokes in Slack, where the people who get them live, and to stop feeding threads to machines that were never invited to the party. I have some sympathy for that view. But the threads are being fed to the machines whether or not I approve, and nobody is asking me first.

Would your last six months of Slack survive a stranger reading it with total confidence and no context? Mine would not. I suspect yours would not either.

---

Screenshots from the original 2018 post. The poo, as established, was a compliment.

I use Claude Code every day, and I love it. Ever since the ChatGPT wave of 2022, we have been hearing that the work of programming is about to be automated away. I teach in a computer science department, so I watch it land from the front of the room: fewer students each year want to learn to program, and I hear the same prediction from colleagues who have written code their whole lives.

The prediction of the end of programming is not new.

I’ve grown suspicious of it, because I’ve now read it with a date attached, and the earliest date is 1959.

Here is the pattern. Every ten or fifteen years since then, someone announces that programmers are about to become unnecessary. The pitch barely changes: the machine now speaks your language, so the specialist in the middle can go home. And every time, two things happen that don’t fit the prediction. The specific kind of programming under attack really does fade. And the number of people who program goes up. Not sideways. Up.

The people who make this prediction are not stupid, and they are not dilettantes. They are experienced industry leaders, academics, and journalists who have spent their lives around programming, and they are genuinely convinced the end is near. That is what makes the pattern worth taking seriously rather than laughing off.

And still, the prediction keeps being half right in the way that makes it feel completely wrong.

1959: the machine will speak English, so you won’t need a programmer

COBOL was designed in 1959 and 1960 by a committee that, as the record puts it, “agreed unanimously that more people should be able to program.” The language was to “make maximal use of English” and be “suitable for inexperienced programmers,” even at the expense of power. That’s why COBOL reads like MOVE amount TO total instead of a row of symbols. The hope riding on top of it was louder than the spec: if the code looks like English, a manager could read it, maybe even write it, and the programming priesthood would lose its monopoly.

Sixty-five years later, managers still do not write COBOL. But plenty of people who would never have called themselves programmers ended up writing something. The circle of people who program got wider. It did not close.

1965 and 1967: the machine will think, so it will program itself

Then the general optimism arrived. In 1965 Herbert Simon wrote that “machines will be capable, within twenty years, of doing any work that a man can do”. In 1967 Marvin Minsky wrote that “within a generation … the problems of creating ‘artificial intelligence’ will be substantially solved”. Writing programs was quietly filed under “any work a man can do.” If the machine was about to do everything, it was certainly about to do this.

1973: then the money stopped

The trouble with a promise that large is that it can be defunded in a single document. In 1973 James Lighthill delivered a report to the British Science Research Council that concluded, flatly, “in no part of the field have the discoveries made so far produced the major impact that was then promised.” The British government used it to end most academic AI funding. The first AI winter followed. The lesson I take from Lighthill is not that the skeptics were right. It’s that overselling has a bill, and when it comes due, the honest work gets cut alongside the hype.

1981: application development without programmers

The eighties opened with the promise moved into the product name. In 1981 James Martin published a book literally titled Application Development Without Programmers, which is where the term “fourth-generation language” got its formal start. The same year, a small British company shipped a program called The Last One. Its creator explained the name: it was meant to be “the last human-produced program that needs to be written.” You picked options from menus and it generated the BASIC for you.

What actually came of the 4GL wave was SQL, spreadsheets, and report builders. Every one of those let more people do more without a programmer. Every one of those also created new categories of work, and demand for programmers kept climbing straight through the decade that promised to end it.

1982: an entire country bet on it

Japan’s Ministry of International Trade and Industry launched the Fifth Generation Computer Systems project in 1982: roughly ¥57 billion, about 320 million dollars, over ten years, to build machines that reasoned in logic and talked to people in something close to natural language. It is now generally filed as a commercial failure. Ordinary hardware from Sun and Intel outran the specialized machines before the decade was out.

1987: “a profession with no future”

The feeling that this time is finally different is not new either, and I have a clipping to prove it. On Friday, 4 September 1987, the Israeli daily Maariv ran a piece under the headline “תכנות - מקצוע ללא עתיד”: programming, a profession with no future.

Maariv, Friday 4 September 1987. The headline reads “programming, a profession with no future.”

It quotes a specialist, Ezra Ben-Kochav, making a case that would sound at home in any 2026 keynote. “The programming component in systems keeps shrinking over the years,” he says. The cause, in his telling, is the arrival of fourth-generation languages, “artificial-intelligence languages,” and application generators, tools that demand far less professional knowledge and cut a project’s development time in half. Operating systems, he adds, are getting friendly enough that you need less skill to run them.

Then comes the line that made me keep the clipping. Ben-Kochav cites studies from the United States showing that fewer and fewer students were choosing to study computer science, and names the reason: the shrinking demand for people in the field. I read that, thought of the drop in my own department, and then checked the date. Thirty-nine years ago. In the decades that followed, the profession it was burying became one of the largest and best paid on earth.

The tell: even the replacement was called an apprentice

Here is the detail that convinced me the pattern is real and not just a run of bad marketing. The most serious academic attempt to automate programming in that era, MIT’s Programmer’s Apprentice (Charles Rich and Richard Waters, from the mid-1970s on), was explicitly designed as an assistant, not a replacement. The apprentice handled the mundane details; the human made the higher-level connections and checked the apprentice’s work.

That is almost exactly the division of labor I have with my AI assistant today. The people who understood the problem best, forty years ago, landed on “apprentice,” not “successor.” They had the right word the whole time.

What the numbers actually did

In 2000 the two occupations were the same size, about 700,000 workers each. By 2019 “software developer” had grown to 1.71 million while “computer programmer” fell to 425,000. They are related but distinct jobs: a developer analyzes needs, designs the software, and builds it; a programmer writes code to a design someone else produced. Source: BLS Current Population Survey (full-time wage and salary workers), via FRED. The developer series was retired after 2019 in a reclassification; BLS counts about 1.7 million software developers in 2024.

This is the part that makes the whole cycle legible, once you notice that “programmer” and “developer” are not the same job. A computer programmer, in the way the statistics count it, writes code to a design somebody else handed over. A software developer figures out what to build, then builds it. The narrow role is the one that’s dying: it stood at 121,200 jobs in 2024, which Fortune reported is the lowest level since 1980.

So the work didn’t disappear, and it didn’t simply change its name badge. It moved up a level. The job of turning a finished spec into code, the part a machine can most plausibly take, shrank. The job of deciding what the spec should be, and standing behind it, grew to about 1.7 million software developers, median wage $133,080, with another 15 percent growth projected over the decade. Every wave of “no more programmers” took aim at the narrow role and kept missing the broad one, because the broad one is mostly deciding, and deciding is the part nobody has automated.

So is this time different?

Yes and no.

The yes is real: the machine genuinely writes the code now, in a way no 4GL ever managed. But look at what writing the code always was. Writing the syntax was hard the way a chore is hard, real skill and real hours, and easy to mistake the effort for the essence. It was a “chore,” though, not the “mission.” The mission was to decide, exactly, what the program should do, and to answer for that decision. That is the programming, and it is the one thing sixty-five years of tooling never took off our hands.

Read the whole list again through that lens and it stops being a run of failed predictions and turns into a single, patient process. Each wave automated a chore and left the mission alone. COBOL took the chore of writing assembly. The 4GLs took the chore of hand-building the same forms and reports. The AI is taking the chore of writing the syntax. None of them touched the mission, because the mission was never the typing. The prediction keeps failing for one reason: from the outside, the chore looks like the job. It is the visible, effortful, teachable part, so people mistake it for the point. It never was the point.

In my own week, the AI’s most valuable move isn’t writing the code. It’s the command that stops and makes me state my assumptions and answer “why” before it builds anything. That is specifying: pinning down what the thing should do precisely enough that even a machine can’t wander off. Specifying is the mission with the typing stripped away, and no wave of tooling ever made it easier.

If I had to bet on where the job goes next, I’d bet up, not out. The work that grows is the work closest to deciding what to build: naming the problem, choosing the shape of the system, drawing the lines between the pieces. We already have a word for the person who does that, “architect,” and it’s telling that the people whose job is to classify jobs keep inventing new versions of it. The US occupational taxonomy had no “database architect” code until 2018; it added one because the role had quietly become real. I’d expect more of that. Not “no more programmers,” but the center of gravity of the work sliding toward whoever decides the structure, whatever we end up calling them. Will we keep calling them “software developers”? “programmers”? “architects”? “product managers”? I don’t know. But the work is moving up the abstraction ladder, and the machine keeps taking the rung below. It writes more of the code every year; a person still has to be accountable for what the code is for.

A schematic, not data. Each wave of tooling automates the rung below, and the human work climbs to the next one. The top rung, deciding what to build, is the one that has never automated. The “architect” rung is my guess at the next name for it, not a measured trend.

So I might be completely wrong. Everyone on this list was certain, and most of them were wrong, which means certainty is clearly not the safe side of this bet. What I’ll commit to is narrower. I’ve now watched the “no more programmers” headline get published, with a straight face, roughly once a decade since 1959, over a line that never stopped climbing. The next time it runs, notice that you’ve read it before. Then ask for better odds than “this time for sure.”

The short version.

You’ve paired with an AI coding assistant by now. You know the two faces of it. For ten minutes it’s the sharpest junior engineer you’ve ever worked with. Then it confidently does the wrong thing, because it guessed at something it should have asked you about, and you spend the next hour unwinding the guess.

People assume the fix for that is a better model, or a cleverer prompt. Mine wasn’t. My Claude super tool is a folder of boring markdown files.

Over the past months I took the software-engineering habits I’d otherwise have to remember to apply, and wrote them down as Claude Code slash commands. Now the habits run themselves. I put the whole set on GitHub as claude-shipyard. Here is what a normal day with it looks like.

The plan is where the thinking happens

Got an issue to fix? I run /make-plan.

It explores the current state of the code, builds a plan, and actively hunts for open questions. When it finds one, it doesn’t guess, which is the whole point. It lays out the alternatives with their pros, cons, and implications, and lets me choose.

And the top of every plan lists the assumptions we’re making. That sounds like a formality. It isn’t. A wrong assumption doesn’t announce itself. It sits there quietly and turns into a wrong step three commits later, when it’s expensive to undo. Reading the assumptions first is the cheapest bug-catching I do all day. State your premises before you build on them. It’s the least glamorous idea in Jean-luc Doumont’s toolkit and the one I lean on most.

One command to start clean

/git-work-on-issue takes it from the very top. It marks the GitHub issue as “in progress”, prepares a worktree and a branch, then calls /make-plan for me.

One command, and I’m working in an isolated checkout with the plan already drafted. My main branch never gets touched. If the whole thing turns out to be a bad idea, I throw the worktree away and nothing else knows it happened.

Brainstorming is just interrogation

Not everything starts as a tidy issue. Sometimes it starts as a vague “I think we should build X, but I haven’t thought it through.”

For that I have /brainstorm, and /brainstorm interrogates me. It opens with “why”, and then it asks, and asks, and asks. Question after question, each one narrower than the last, until everything is clear. It’s slower than I’d like. That slowness is the point.

The reason I keep it around is that the relentless questioning is the only reliable way I know to surface the unknown unknowns: the decisions I didn’t even know I hadn’t made yet. Those are the ones that sink projects. Not the hard problems you can see coming, the small ones you never noticed you were quietly assuming.

From a fuzzy idea to merged PRs

The output of a /brainstorm doesn’t just sit in a document. It can become a GitHub milestone, split into issues. From there, /milestone-plan and /milestone-run take the whole pile and work it one issue at a time: plan, implement, review, merge, next. I go from “I have a fuzzy idea” to “there are merged PRs” without personally babysitting every step in between.

The unglamorous end, where the code actually gets good

The interesting part of software is the thinking. The part that decides whether the code is any good is the boring stuff at the end, and the boring stuff at the end is exactly what I skip when I’m tired. So I wrote that down too.

• /git-pre-pr self-reviews the diff before I open a PR: tests, leftover secrets, sloppy exception handling, the things I’d be embarrassed by in review.
• /gh-code-review reads the review comments back to me grouped by severity, so I fix what matters before I go bikeshed a variable name.
• /git-pr-merge merges and cleans up the branches and worktrees behind it, so I don’t leave a graveyard of stale branches.

Why a folder of text files beats a better prompt

Here’s the part I didn’t expect. None of this is clever AI. There’s no fine-tuning and no secret prompt hiding in the repo. It’s years of software-development practice, written down as plain markdown.

What the markdown buys me is that the discipline stops depending on me being disciplined. On a good day I’d remember to list my assumptions, question my own plan, and review my diff before pushing. On a tired day, a Friday-afternoon day, I wouldn’t. The commands don’t have tired days. They carry the process so I don’t have to.

That, for me, is where AI-assisted coding actually pays off. Not a smarter model. A model that runs your process, the same way every time, including the times you would have cut the corner yourself.

The caveat

This fits how I work. It might not fit how you work, and some of these commands encode opinions you’d reasonably disagree with. I like worktrees; plenty of good engineers find them more trouble than they’re worth. So don’t adopt it wholesale. It’s all open source. Read it, take the two or three ideas that map onto habits you already have, and leave the rest.

Steal what’s useful.

“I only care about what a few people think of my work and they are already aware of what I produce. Think of me as a ‘professional loser.’”

A researcher wrote that to me last week. I’d cold-emailed them to pitch Loud Camel, the thing I’m building, and instead of the brush-off I expected, I got two thoughtful replies and a PDF: A. C. Leopold’s 1973 paper “Games Scientists Play,” the one that coined “professional loser.” They wanted me to know which segment of my market they belonged to, and to register that they considered the label, in their words, “silly and testosterone-driven.”

I want to defend them, mostly. And then I want to point at the single assumption holding their position up, because I think it’s quietly breaking.

What Leopold got right, and what’s ugly about it

Leopold describes scientists chasing prizes, gaming citation counts, publishing in prestige journals even when, he notes, “most people who are interested in the subject of your paper may not read that journal.” Swap a few nouns and he’s describing LinkedIn. He wrote the attention economy in 1973, before anyone called it that.

The ugly part is the title. A scientist who won’t compete for attention is, to him, “tantamount to being a professional loser,” and he found an “alarming proportion” of them. That’s the part the researcher rejected, and they’re right to. Reticence isn’t a moral failure. Some of the best people I know would rather be correct than be noticed.

The loser’s bet, stated fairly

“The few people who matter already know my work.” For a human field, that isn’t denial, it’s an accurate model of how reputation actually moves. Leopold himself, later in the same paper, lands on the same mechanism: scientists run on what he calls “strokes,” small signs of recognition, and “a stroke is only as good as the stroker.” Being known by the three people who define your subfield is worth more than being seen by ten thousand strangers. The professional loser has simply noticed this and refused to chase the strangers. Rational.

It even has range. The researcher granted, generously, that “being noticed is better than the alternative,” only that it is “necessary but not sufficient.” I agree with every word.

The assumption underneath it

Here’s the load-bearing assumption, the one nobody states because until recently it never needed stating: the people who decide whether your work gets found are people.

That’s the part that’s changing. More and more, the first pass over the literature isn’t done by the three colleagues who know your name. It’s done by a model. Someone asks ChatGPT or a research tool what’s known about X, and the tool returns what it can retrieve and silently drops the rest. A paper nobody can find isn’t judged on its merits. It just isn’t in the room.

Reputation-agnostic is not obscurity-proof

The researcher saw this coming, partly. They wrote that by “being more agnostic to reputation,” LLMs “may erode current practices.” True. A model doesn’t care that you’re a full professor, or that you publish once a decade. The optimistic read is that this rescues the professional loser: a reputation-blind reader should surface good obscure work on merit, no self-promotion required.

I don’t buy it, and the reason is one short distinction. Agnostic to reputation is not the same as agnostic to findability. The model doesn’t skip your paper because it’s unimpressed by you. It skips your paper because it can’t retrieve it. Reputation-blind, yes. Obscurity-proof, no.

This is what actually changed for the professional loser. The old stance was protected by human colleagues who carried your work around in their heads and brought it up when it was relevant. They remembered you. The model remembers no one. It doesn’t snub the obscure, it just can’t reach them. “The few who matter already know my work” was a fine bet while the few were people. It gets shakier every quarter that the few include something that has never heard of you and never will.

I might be wrong

The honest hedge: maybe the tools get good enough that retrieval stops rewarding the findable and starts genuinely finding everything, indexing the forgotten preprint and the badly titled 2009 paper as readily as the loud stuff. If that happens, the professional loser was right all along and I’m selling umbrellas in a drought. It’s possible. I’d just rather my work be in the index while we find out.

I wrote the broader, less science-flavored version of this argument over in my newsletter, On professional losers. And I owe the whole train of thought to the researcher who called themselves one, and then handed me a 53-year-old paper to argue with. The best kind of reply to a cold email.

Where is my $400,000?

Do AI researchers know what a citation is worth? Do economists, the people who literally study what things are worth? No. They write for the science, the result, the next question. The price of a citation never comes up.

Do you know what your citation is worth?

No. Nobody told you, because you were busy doing the work.

Albert-László Barabási put a number on it. In his 2018 book The Formula, he treats citations as currency and sets an exchange rate: take what the United States spends on research, divide by the citations that money produced, and you land at roughly $100,000 per citation.

Since I launched Loud Camel, a tool that helps researchers get cited and recognized, I have picked up four new citations. So where is my $400,000?

Why the $100,000 citation is an average, not a price

It is an average, and a treacherous one, because it sits on top of one of the most lopsided distributions in science. Citations follow a power law. Most papers are cited well below the mean, a large share are never cited at all, and a small elite collects the bulk of the total. An average over that shape tells you about the elite, not about you. It is the street where everyone is a millionaire on paper because Bezos just moved in.

The skew is also getting worse. Mathias Wullum Nielsen and Jens Peter Andersen, writing in PNAS in 2021 across 4 million authors and 26 million papers, found the top 1% of scientists lifted their share of all citations from about 14% to 21% between 2000 and 2015, with the Gini coefficient rising from 0.65 to 0.70. The detail that matters: over the same years the elite’s citations per paper actually fell, from 3.10 to 1.79. Their share grew while their per-paper impact shrank. Concentration tracks position and volume, not better science.

Does the money side hold up at all?

Partly, and it is only fair to say so. Funding does buy citations: an instrumental-variable study of China’s National Natural Science Foundation found competitive grants raise both the output and the citation impact of the work. Public research earns large real returns to the economy through spillovers. So Barabási is not inventing value out of nothing.

But two things puncture the tidy $100,000. First, the dollar figure is an average over the whole national bill, not the price of your marginal citation. Second, the citation is a weak and gameable yardstick: counts and impact factors are inconsistent predictors of research quality, and once a number becomes a target, paper mills, citation cartels, and self-citation rings move in. Goodhart’s law does not exempt scholars.

So whose citation is worth $100,000?

Not the average researcher’s, because the average is a fiction the giants create. The value of your next citation is decided by where you sit in a distribution that is getting steeper every year, and position there is set less by how good the work is than by how many of the right people ever find it.

So I will end where I started, with a question. Whose citation is actually worth $100,000? And what are you doing this month to make yours one of them?

I finished the billing months ago. I never switched it on.

Loud Camel has been live for months. People sign up and use it, all for free. The billing has been finished almost that whole time. It works, I tested it, it is ready to turn on. And week after week, I have quietly decided that this is not the week.

Each time, I had a reason, and the reasons were real, which is exactly what made them work. There are few users, so what is the hurry. The ones who do sign up, I upgrade by hand, because I want them to enjoy the product without thinking about a credit card. Traffic is low. The timing is never quite right. None of these is a lie. Put together, they made a wall I did not have to climb, and I told myself I was being patient and generous.

That is the part worth noticing. This was not patience and it was not generosity. It was procrastination wearing their clothes. Ordinary procrastination feels bad while you do it; you know you are avoiding something. This kind feels responsible. Every week I chose not to ship billing, I felt a small sense of relief, and I read that relief as proof I had made the sensible call. It was the opposite. The relief was the tell.

Underneath the sensible reasons was something smaller and less flattering. As long as the product is free, “people use it” can quietly pass for “people need it.” The day I ask for money, those two stop being the same sentence. And I did not want to learn which one was true. If I turn on billing and nobody pays, that is not a bug I can fix over a weekend. That is the market telling me it does not need the thing I have been pouring myself into. So I left the test un-run, and the question comfortably open.

But that comfort was bought with the wrong currency. Free usage was never the signal I needed. People will take anything that costs nothing, and the free upgrades I handed out by hand were, if I am honest, me manufacturing the appearance of demand for an audience of one: me. The only real evidence that work matters is that someone is willing to pay for it. By hiding from the answer I was afraid of, I was also turning away the only answer that would have meant anything. Months of activity, none of it able to speak to the single question worth asking.

And the test does not get easier by waiting. The answer is already whatever it is. Delaying only postpones the moment I learn it, while I keep building on an assumption I have refused to check. Fear felt like safety. It was the more expensive option the whole time.

There is an irony I cannot pretend not to see. I spend my days building a tool that helps researchers stop hiding their work and get it in front of the people who should see it. There is a Hebrew saying, מי שמתבייש מתייבש, the shy one dries up. And I had been too shy to put a price on my own work and ask to be paid for it.

So I stopped waiting. By the time you read this, billing is live on Loud Camel. I still do not know what it will tell me, and that is the point. I would rather find out than spend another month not knowing.

If you are sitting on something you keep deciding not to do, and every reasonable excuse to wait shows up with a small wave of relief, look harder. It is usually pointing at the test you are most afraid to run.

What you just read is a form of Omphaloskepsis, navel gazing, a term and a technique I learned from my former manager Martin Remy. Done honestly, it is how you catch yourself rationalizing before the rationalization costs you.

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.