An exploration of baby name trends in the USA from 1880 to 2025

Table of Contents

1 Introduction

Baby name rankings are very popular on the internet and on social media. A typical ranking is the top baby names for each gender in the USA in 2025 (source):

What these rankings often don’t show is the quantitative values behind these rankings. That is, how many babies were given each name in the year 2025, and how does that compare to previous years?

Thankfully the USA Social Security Administration (SSA) releases the full dataset for baby names going back to 1880. I took this data, and summed up the counts of the top ranked names each year, and plotted them against each other. See the above graph.

From this graph we can see that the popular names are getting less popular, while the number of births is still in line with what it was 50 years ago. This is something that plain rankings obscure.

If “Liam” or “Olivia” were ranked in previous years with the same frequency as 2025, they would be ranked noticeably lower than #1. For example, in 2000 “Liam” would have been ranked 11th and “Olivia” would have been ranked 12th. In 1950, “Liam” would have been ranked 19th and “Olivia” 27th. But names are more spread out these days, so their frequencies are sufficient to be ranked #1 in 2025.

The other sections present other charts and insights derived from the data.

These are the top level findings:

• There has been an almost 20× increase in the number of births each year from 146 years ago.
  • The 5 year average increased 18.6× from 215,000 births in 1885 to 4.02 million in 2025.
• These days there are more than 15× names to chose from than 146 years ago.
  • From 1000 names each for boys and girls in 1880, there are now more than 17,000 unique names for girls and more than 14,000 unique names for boys each year .
• However, given all this choice, the names frequencies are still heavily skewed towards a relatively small proportion of names.
  • In 2025, the top 100 names were given to more than 35% of newborns.
  • Over the whole 146 years, the top 100 names account for 46% of all names given.
• That said, the top ranked names are less popular both absolutely and relatively since the 1900s, and this trend is continuing.
  • The frequency of the top 100 males names has decreased absolutely by 2.5×, from covering 1.62 million of all births in 1956 to 650,000 births in 2025. Relatively they decreased by almost half, now accounting for 38% of all births from of a peak of 81% in 1880.
  • The frequency of the top 100 females names has decreased absolutely by 2.5×, from covering 1.31 million of all births in 1957 to 514,000 births in 2025. Relatively they decreased by more than half, now accounting for 31% of all births from of a peak of 77% in 1880.
• Girl names are consistently slightly more diverse than boy names.
  • Over the whole period there were on average 40% more girl names than boy names each year. Over the last 5 years, there were 24% more girl names on average.
  • Over the whole period the top 100 girl names accounted for 10% less of girls than the top 100 boy names did for boys. Over the last 5 years the 100 girl names have accounted for 7% less on average.

2 Methodology

2.1 Data

The data is the “National data” released by the USA Social Security Administration (SSA) at SSA: Beyond the Top 1000 Names, released in 2026. It has the following limitations:

1. Not every US citizen has a social security number.
2. Only names with more than 5 births per year are reported.

The dataset consists of 146 CSV files for each year from 1880 to 2025. Each CSV file has three columns: name, gender (M or F) and frequency. The CSV files are ordered by gender (F than M), then descending frequency and then alphabetically for tied frequencies.

2.2 Dense Ranking

I used a dense ranking. This means that identical counts are given the same rank and there are no gaps in the rankings. So the top 100 ranked names could account for more than 100 names in a given year. However, ties in the top 100 names are rare, but they get more common as the counts get lower. At the lowest values there can be up to 2000 names per ranking.

No single year exceeded a dense ranking of 1000 per gender despite some years having move than 20,000 unique names for a single gender.

2.3 Code

I used Julia to analyse the data. I will not present the full code here, but here are some snippets.

Loading a single file and transforming:

using CSV, DataFrames
filepath = joinpath(data_dir, "yob2025.txt")
df = CSV.read(filepath, DataFrame; header=["Name", "Gender", "Count"])
transform!(
    df,
    :Name => ByRow(x -> x[1]) => :FirstLetter,
    :Name => ByRow(length) => :Length,
) # name composition
transform!(groupby(df, :Gender), 
    :Count => cumsum => :CumulativeCount,
    :Count => (x -> x ./ sum(x)) => :Frequency,
    :Count => (x -> denserank(x, rev=true)) => :Rank,
) # gender ranks
transform!(groupby(df, :Gender), :Frequency => cumsum => :CumulativeFrequency)

Filtering on gender:

m_df = filter(:Gender => ==("M"), df);
f_df = filter(:Gender => ==("F"), df);

Quantiles:

idx = something(
    findfirst(m_df.CumulativeFrequency .>= 0.5),
    nrow(m_df)
) # rank of 50% quantile / median

Loading multiple files and joining into one large dataframe:

using CSV, DataFrames
using Parquet2
filepath = joinpath(data_dir, "yob1880.txt")
df = CSV.read(filepath, DataFrame; header=["Name", "Gender", "1880"])
for year in 1881:2025
    print("$(year), ") 
    filepath_next = joinpath(data_dir, "yob$year.txt")
    next_df = CSV.read(filepath_next, DataFrame; header=["Name", "Gender", "$year"])
    df = outerjoin(df, next_df, on=[:Name, :Gender])
end
year_matrix = df[:, string.(1880:2025)]
df.Total = sum(eachcol(coalesce.(year_matrix, 0)))
df.Count = sum(eachcol(.!ismissing.(year_matrix)))
size(df) # (117820, 150)
Parquet2.writefile("names_ssa_1880-2025.parquet", df)

Transforming the joint dataframe:

transform!(groupby(df, :Gender), 
    :Total => (x -> denserank(x, rev=true)) => :TotalRank,
) # gender total ranks
transform!(groupby(df, :Gender),
    [y => (x -> denserank(x, rev=true)) => "Rank$y" for y in years]...
) # gender yearly ranks
transform!(df,
    :Name => ByRow(length) => :Length,
    :Name => ByRow(x -> x[1]) => :FirstLetter,
); # name composition

Export data to JSON:

using JSON
out = Dict{String, Any}("years"=> 1880:2025)
names_to_save = Dict("M"=> ["John"], "F" => ["Mary"])
for gender in ["M", "F"]
    out[gender] = Dict{String, Any}()
    gender_df = gender == "M" ? m_df : f_df
    for name in names_to_save[gender]
        idx = findfirst(gender_df.Name .== name)
        out[gender][name] = Dict(
            "count" => Vector(gender_df[idx, years]),
        )
    end
end
JSON.json("output/names.json", out)

3 Top Names

The “Top 1” dataset from the Top N graph can be decomposed into the top names each year. This produces the following graphs:

These graphs show how relatively “unpopular” the most popular names are now compared to the popular names of the 1900s.

The number of names that have reached the top spot is very small. There are only 19 in total, 8 boy names and 11 girl names. Mary alone was the #1 girls name for 76 years, more than half the total period from 1880 to 2025.

Here is how these top yearly names are ranked across all 146 years:

There are gaps here because many popular names have never been ranked #1. For example, “Elizabeth” is the overall #2 female name, but there was never a year it was ranked #1.

4 Unique Names

The number of unique names has grown from 2000 names in 1880 to over 31,000 names in 2025. Every year has seen names added and removed from the list, with up to 4,000 removed and added each year in the 2020s. Overall there are 117,820 unique names in the datasets. Of these 31,227 (26.5%) are represented in 2025.

The above graph shows how skewed the dataset is, with the top 75% quantile line (75% of all baby births) hovering at around 4% of all names.

Girl names are consistently slightly more diverse than boy names. Over the whole period there were on average 40% more girl names than boy names each year. Over the last 5 years, there were 24% more girl names on average.

Some insight can be gained by looking at the ratio of the total count each year (the total number of births) to the count of unique names each year, taking into mind the heavy data skew towards popular names. From this graph we can see that names were most concentrated in the 1950s, with about 470 names per birth for boys and 300 names per birth for girls. These ratios have come down almost 4×, and now sits at 120 for boys and 91 for girls. This implies a greater diversity in naming in recent years.

5 Composition

We can also investigate the composition of the names in the dataset. Here I do so for the first letter and also for the name length.

Over the whole period the most popular first letter for boy names was “A” (Anthony, Andrew, Alexander) and “J” (James, John, Joseph), and for girls was also “A” (Anna, Ashley, Amanda) followed by “S” (Susan, Sarah, Sandra).

If we were to take a random person at any year in the period, for a man their name would most likely start with a “J” while for a woman it would most likely start with an “M” (Mary, Margaret, Michelle).

The names vary in length from 2 letters (Al, Ty, Jo, Lu) to 15. (Many of the 15 letter names look like concatenations of shorter names and might be mistakes e.g. Muhammadibrahim, Christopherjohn, Mariadelosangel.) Most names are 5 to 8 letters long.

6 Bonus

My name is one of the many rare names. It is a Hebrew name, spelt as ליאור and transliterated as “Lior” or “Leor”. It is gender neutral. There is also a female only version, ליאורה, which is transliterated as “Liora” or “Leora”.

The data shows that “Leora” has been used in the USA since at least 1880, but “Leor” was only first used in 1979. It is ever so slightly gaining in popularity, with 93 baby boys and 473 girls given a variation of the name in 2025. For girls, the “Liora” spelling recently overtook “Leora” in popularity.

Open source software has always been a cornerstone in scientific applications.

From supercomputers to CERN labs, handling some of the greatest discoveries of humankind and accelerating particles beyond imagination, open source software has provided the framework for all necessary technological usage.

Bringing it down to a simpler level, KDE's Step provides you a platform to test out some of the most important basic concepts in physics, like simple motion, electrostatics and gravitation, and even things like spring (harmonic) motion.

Developing an intuition about these phenomena can finally bridge the knowledge gap that students need. So if you are a physics teacher (or student), KDE Step is worth your attention.

Interface and Experience

Using the basic KDE design kit, the application looks quite familiar as it is. It is arranged in a very efficient manner, with all the usable objects on a panel on the left side of the window, while the right side holds the panel that can be used to modify any of the attributes of those objects as well as a panel that shows the history of the steps (no pun intended) made by the user. On the top of the window, all the menus are present with the undo/redo buttons, and most importantly, the button that allows you to start the simulation.

To demonstrate the elements and how they're used in the best way possible, I'm going to show different simulations that incorporate said elements. It is the most efficient and vivid way, since it is, after all, a simulation app.

Simple Harmonic Motion

As a very famous quote from Sidney Coleman says, "The career of a young theoretical physicist consists of treating the harmonic oscillator in ever-increasing levels of abstraction." Keeping up with that sentiment, I will show a very basic demonstration of a simple harmonic motion.

The elements used here are two particles, a spring, a graph, weight field and an anchor. Particles in Step are simple zero-dimensional point objects with modifiable position, color, velocity, mass, momentum and kinetic energy.

Springs are simple, you can attach both ends to objects, you can change the stiffness. Anchors are utilities that can be used to fix the position of an object to the scene. No matter what, it will not move from where it is placed.

A weight field simply simulates the gravitational force of earth for all the objects placed on the scene, but again, you can modify the gravitational acceleration to suit whatever kind of simulation you're trying to run (for example, trying to simulate the gravitational force on the moon).

Finally, the graph utility can be used to plot any property of any object on the scene against any other property.

Soft Body

While sounding like a promise made by a moisturizer, soft body is not that but a category of objects in physics that are not rigid but that deform and change shape according to the parameters set.

More accurate, and as shown in the app itself, it can be thought of as an object made of small particles connected to each other by springs that deform according to the force provided.

Two new elements are used here, a soft body (that has already been described) and a box. A box is just that, a rectangle with modifiable dimensions, where apart from what you can already change in a particle, you can also change the angular velocity, angular momentum, inertia, and so on.

Orbit

Another basic simulation that can really help is that of an orbit. Step provides a gravitational field simulation, in which the universal law of gravitation starts holding true and applying within the canvas.

In this simulation, I've modified the value of the gravitational constant to something that allows my particle to orbit the central particle (because I finally can), and I'm using a controller to change the mass of my central particle while the simulation is going on to show how that changes the velocity and distance of the revolving particle.

As you can see, for the first part of the video, it is making a calm orbit but as soon as I start increasing the mass, the particle comes closer (as one would expect) and when I decrease it, the particle goes out the frame (a little dramatic, but still expected).

Compound Pendulum

Have you ever wondered what an oscillating lambda would look like? Well wonder no further because Step allows you to make any kind of polygon that you would like to make with the polygon tool, and then you can use a pin to fix the position of one point in that body to the canvas. And some weight force to the scene, and there you go. A lambda pendulum:

This kind of pendulum that isn't one concentrated mass but distributed instead is called a compound pendulum in physics, which can be quite difficult to visualize sometimes.

Linear-Angular Parallels

Students often struggle with the equations for the motion of a disk, or anything that has to do with rotating rigid bodies, but it is only a matter of translation of the values in the usual linear equations of motion into those that concern rotating bodies. For example, mass gets replaced with moment of inertia, velocity with angular velocity, same with acceleration and so on. In the following simulation, that's exactly what we're showing:

In this simulation, the particle and the disk have mass and moment of inertia with the value 1, velocity and angular velocity with value 6, acceleration and angular acceleration -2 respectively.

As you can see, the changes happen hand-in-hand, making it clear how the equations work practically parallelly. I have used a linear motor to apply a linear force to the particle and a circular motor to apply a torque to the disk. The values on display can be shown using the meter utility.

Stable and Unstable Equilibrium Positions

In the first case, I've fixed two positive charges of equal magnitude on the canvas with anchors. Another positive charge was placed right in between them. The charge, of course, will be in equilibrium just by the virtue of being smackdab right in the middle of the positive charges. What happens if I slightly move the central charge from its position?

The charge starts oscillating. In a real life scenario where there are losses due to friction and so on, this will return to the equilibrium position right in the middle. But what if my central charge is negative? What happens then?

As you can see, the charge moves on to the side of movement, as you would expect. In this case, the equilibrium was unstable, meaning even the slight change in position on one side will result in absolute ruin of the equilibrium state. I've used charged particles, which are similar to normal particles but with the added option of adding a charge to them. Similar to how we did with gravitation, you need to add the Coulomb field to the canvas in order for the law of electrostatics to start applying.

Constraints

A lot of basic physics is based on constraints, which can be of different sorts. The most basic one is where the distance between two bodies is fixed, so that the motion of one of the bodies impacts that of the other. So in this simulation, I've done that exactly with a massless stick, which connects two bodies in Step. I've given a certain velocity to one of the particles, and you can see here how it impacts the other one:

Perfect Gas Simulation

Finally, Step has a tool that lets you simulate a perfect gas, following the basic principles of kinetic theory of gases. When applying it on the canvas, you can configure the area that the gas will exist in, the number of particles inside that area, the concentration, the temperature, particle mass and mean velocity. Sure, some of these things are dependent on each other and all of them being configurable individually does seem a little counter-intuitive, but if you change one of the values that another depends upon, it changes automatically. There's no disregard for the physics of it here.

Wrapping Up

There are some very obvious points at which Step breaks. Not even showing an error, it just breaks. For example, if you configure the mass of a particle to be 0 or very close to it, for any simulation that involves forces or collisions, the canvas just disappears. Obviously, massless particles are not in the scope of scenarios which Step can simulate.

Overall, Step has some excellent options that can really help students visualize their physics lesson up to an elementary undergraduate level.

As a student of Physics, I have been using it for years to clear my doubts, but it is only obvious that the simulation can only be as helpful and accurate as you are careful with setting it up. More than that, it helps you explore possibilities that aren't possible in the physical world, such as completely ideal conditions of zero friction, the ability to change fundamental and universal constants and so on.

On a related note, you may want to check out the list of distros for schools and education.

I hope this article was helpful and that you have fun seeing the answers to your physics doubts come to life. Cheers!

On college campuses around America, old-fashioned blue books are making a comeback.

Around a century ago these standardized booklets for written exams were introduced by Butler University in Indianapolis. For many former students of a certain age — myself included — the sight of a blue book can generate anxiety and even nightmares. And many former instructors — myself again included — recall the tedium and strained eyesight of trying to decipher students’ handwriting. So it was an improvement when exam-taking shifted from paper to computer. Or so it seemed at the time.

Now, however, students are using AI to write essays and answer questions on take-home exams, as well as taking in-person exams on their computers. As a result, an AI-arms-race has developed between instructors and students. Concerned that students are not doing the work themselves but are simply copying and pasting AI output, instructors have begun using AI programs to detect students’ use of AI. Inevitably, there are now AI programs that students can use to outwit the instructors’ AI detection programs.

So, not surprisingly, many instructors are going back to handwritten in-class exams, generating a sudden boom in the demand for blue books. Ominously, even the return of in-person testing may not solve the problem of testing in the face of AI: Cheating using AI glasses is on the rise in Asia and will doubtless spread worldwide.

My concern here isn’t about testing; it’s about learning. The objective of testing is to further learning, and there is growing concern (as well as evidence) that students’ use of AI damages their capacity to learn. And what we really mean by learning is the ability to think. Students who rely on large language models to answer questions won’t learn how to think by reasoning through the evidence to form a conclusion. As a result, they will be unequipped to deal with situations in which AI either can’t provide an answer or provides misleading answers.

In short, there are good reasons to worry that what we’re calling artificial intelligence will adversely affect the development of our natural intelligence. Moreover, in the case of basic learning, those adverse effects may be virtually irremediable.

The rise of generative AI isn’t a complete departure from an ongoing process of outsourcing human judgment and understanding to external models. Rather, generative AI is just a further step in a process that began a generation ago with the launch of Google search and accelerated with the rise of smartphones. However, ChatGPT and Claude Code ratcheted that process up to a much more rapid pace.

Granted, each stage of this process has brought obvious short-term benefits to those using the new technologies. Yet these benefits have come at the cost of real, measurable long-term damage to human understanding and cognition. And AI, which is already creating a crisis in education, will almost surely make the damage much worse.

Beyond the paywall I will address the following:

1. A brief history of outsourced cognition

2. The sharp deterioration in learning with the advent of smartphones

3. The AI cognitive crisis

4. Will cognitive losses due to AI lead to a new form of inequality?

Read more

The history of education technology is inseparable from the history of standardized testing.

That’s part of the argument I make in Teaching Machines, where I trace the development of both the machinery of teaching and the machinery of testing back to the early twentieth century. There are more recent connections too, of course: the Obama Administration’s push for computer-based testing in the early 2010s, for example, which led many more school districts to adopt one-to-one computing (and, in particular, to not just buy Chromebooks but to adopt the whole Google framework about what digital work – school- and otherwise – should look like. See Natasha Singer’s forthcoming book, Coding Kids.) And while so much of the reporting on the recent and growing anti-ed-tech sentiment has framed the movement in terms of “too much screen-time,” parents (often the very same parents) have long been very frustrated and very vocal about “too much testing.”

As such, it’s more than a little bit strange then that certain politicians and pundits believe that a winning message right now is “bring back high-stakes testing.” Or maybe it’s not strange at all: maybe all this helps to make clear that the technocratic elite care very little about what people want or need. They aren't even bothering to "read the room."

In a recent op-ed in The New York Times, Ross Weiner argues that the calls to bring back the test-based accountability of “No Child Left Behind” is delusional. (Well, to be fair that’s the word that the headline writer chose: "delusional.") Weiner describes these policies as insufficient then and inadequate now. “Young people are placing more emphasis on purpose, relationships and contribution than on older markers of status,” he argues.

For a generation, the reform coalition took its validation from economists and accountability metrics, while treating parents, students and communities as mere functionaries rather than partners in a shared civic enterprise.

Taking their priorities seriously would mean broadening what we expect from the classroom. Schools should put what students can do on equal footing with what they know, embedding real skills in academic learning rather than leaving them to chance or sequencing them to later in life. Schools should reconnect with the communities they serve, so young people learn through and about the places where they live. And they should reanimate the character-forming, developmental mission a pluralistic democracy requires.

Federal policy has an essential role to play in public education: protecting civil rights, funding quality data and research, and encouraging promising practices to spread. But the formative mission cannot be mandated by Washington. Belonging, the foundation of both learning and civic commitment, is relational and starts local; it cannot be standardized or scaled, but must be cultivated by schools that are responsive to the communities they serve.

It’s not a fully-fleshed out vision for education, to be sure, but it does gesture at something quite different from the technocratic one that schools have spent the last few decades delivering -- and delivering via education technology, via a machinery that shapes the form and increasingly the content, the curricula and the pedagogy. Funny, for all the invocation of "the future of education" from ed-tech evangelists and testing companies and politicians, they're almost always talking about the past, or at least about much older narratives of what that future might look like. (And in doing so, they ignore that computers have been ubiquitous in classrooms for a very very long time now.)

I’ve written quite a bit recently about how nostalgia seems to have circumscribed our ability to think about the future -- in particular, how those building and funding and hyping “AI” are still caught in the “Sputnik moment,” in ideas that are now seventy years old. But as this weird fondness for “No Child Left Behind” certainly demonstrates, there are other eddies that seem to have snared the political imagination.

They’ve caught and captured the pedagogical imagination too, I fear -- I’ve noticed this in so many responses to recent efforts -- in Australia, in the UK, and now perhaps in Canada -- to limit children’s access to social media. Instead of banning technology, some people argue, we need to teach children how to use the technology correctly. We need “critical thinking.” We need “literacy.” (Fill in the blank with a descriptor there: Computational literacy. Digital literacy. Web literacy. AI literacy.) It’s almost as if the past thirty some-odd years of these sorts of lessons have made all the difference and -- somehow simultaneously -- have never happened at all.

I’m not certain if folks are stuck in another era or simply long for another (mostly make-believe or misremembered) era, the one in which they imagine that adjectives like “open” and “critical” were sufficient to bend the arc of technology towards progressive education and away from the school-to-prison pipeline (or its contemporary Fortnite-to-looksmaxxing pipeline).

Things have shifted.

I’m still stewing on one of the essays I linked to last week, Fred Turner’s recent article in The Baffler on “The Texas Ideology” -- on a Silicon Valley that has embraced muscular Christianity, white supremacy, political surveillance, military contracts, and (as ever) resource extraction, and on what all this might mean for education and education technology. (Related: “Texas is poised to require millions of students to study Bible stories,” CNN reports.) "The California Ideology," with its privileging of neoliberalism, libertarianism, and individualism, has been the driving force of so much of ed-tech in the last decade or so – the MOOC was exemplary. And now? And next?

I’ve just started reading Quinn Slobodian and Ben Tarnoff’s new book, in which they argue that “Muskism” has replaced “Fordism” as the new model for capitalism -- a model which has major ramifications not just for “business” but for all of society. (Do recall that one of the key elements of Fordism was that factory workers be paid enough wages to buy things: mass production and mass consumption were inextricable.) And while many people might like focus on Musk’s politics and/or his social media persona -- certainly as good an example as any as to why this stuff is bad for one’s mental health -- it’s worth remembering that, at his core, he is a government contractor: that is the business of SpaceX. (Green tech federal money provided some of the funding for Tesla too.) And the infrastructure that Musk has built and controls -- including the Starlink satellites -- also have a nostalgic bent to them, Slobodian and Tarnoff suggest: not just in the science fiction worlds that Musk read about as a kid, but in his lived experiences in apartheid South Africa.

The future, for Muskism, is an apartheid techno-state.

And I’m sorry, but no amount of “critical thinking” or “‘AI’ literacy” is going to get us out of “fortress futurism,” particularly when the very companies building this fortress subscribe to a vision of algorithmic-ordering of children. (Indeed, they long have, when we consider the origins of standardized testing: in intelligence testing and eugenics.)

Something Wicked This Way Comes:

Via Nature: “Is AI ruining our skills? Early results are in — and they’re not good”

“The problem with evidence production on AI in education,” by Ben Williamson

Arjun Appadurai review Theo Baker’s new book on Stanford: “The University as Giant App”

“Your AI is not a tool,” writes L. M. Sacasas. It is a “denial-of-service attack on the human psyche.”

“How to talk about "AI" without adding to the anthropomorphization,” by Emily Bender and Nanna Inie

The words we use really matter. I wonder if, every time someone gets all in their feels about banning social media – this strikes me as language that triggers all sorts of other associations, a lot of them negative, as well as inducing in libertarians their loud regulatory paroxysms (or whatever you call their version of “moral panic”) – we might simply pick different phrasing: this issue, for example involves raising the age limit of who can sign up for these sorts of apps from 13 to 16. I dunno, seems less scary. (Yes, I realize that it's more complicated than that, and that handing over one's ID to access websites seems quite un-good. Then again, there isn't much good in any of the data we blithely surrender.)

Or maybe rather than talking about screen free classrooms (or weekends or whatever) – a phrase that emphasizes the absence of devices but is easily twisted into some sort of lack – we talk about all the things that we are doing instead of staring and scrolling and clicking. This does mean, of course – and this is truly paramount – that we plan for those things, that we reclaim public space for kids, that we fund activities and resources for them and with them. And not because we think they’re broken without their phones, but because we believe that they are whole people, worthy people regardless.

Today’s bird is the prothonotary warbler, which according to Wikipedia, “is named for its plumage, which resembles the yellow robes once worn by papal clerks (named prothonotaries) in the Roman Catholic Church.”

I confess that sometimes my brain misinterprets words – my own little defective autocomplete machine, I guess – and I recently read this bird’s name as “profanatory warbler,” which I thought was delightful. I promptly went to learn all about why the little yellow creature would be cursing/cursed. Oops.

If one were to invent such a reason, it would be perhaps its connection to Alger Hiss and Whittaker Chambers. A Cold War history refresher, we love those here: Chambers had accused Hiss of being a communist spy; Hiss insisted he did not know Chambers. But they both mentioned the warbler in their Congressional testimony before the House Un-American Activities Committee – "gotcha." (Obviously it’s never taken much to convince American government officials that dissenters are part of some vast and violent conspiracy.)

The profanatory warbler’s cry, I imagine, sounds something like “Are you fucking kidding me?!” Over and over and over again.

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Last November I suggested that 2026 would witness a tech backlash of unprecedented intensity. And it’s now happening with a vengeance. Silicon Valley is getting skewered everywhere, and to a degree inconceivable just a short while ago.

Just yesterday, The Economist finally grasped how rapidly tech antipathy is mounting—and made AI backlash its cover story.

The latest survey numbers are devastating. Every demographic group is now opposed to AI—especially young people, previously the most enthusiastic supporters of new tech.

I’ll have some scary stories to share below, but let’s start with a more lighthearted angle. Comedians Harris Alterman and Dave Ross, for example, recently took their parodies of Silicon Valley marketing out into the real world—via a series of make-believe marketing campaigns.

I share a few examples here with permission.

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The first poster is absurd. But it’s also an accurate depiction of the circular accounting behind the AI boom.

AI is also the target here.

Here’s my favorite. And it’s all the funnier if you’ve walked around San Francisco recently and seen meaningless slogans of this sort plastered all over the city.

I’ll share one more. It captures the marked inanity of the current moment, when tech companies are in a race to monetize the impoverishment of their own workforce—and the population at large.

This mockery of tech is even showing up in traditional comedy settings—for example Saturday Night Live.

Not every pushback to encroaching tech is quite so gentle.

Consider the case of “Mr. Daniels,” a 25-year-old man from England. He knows that AI will rob every music file on the web for training—so he decided to poison the data.

How did he do it? According to Tuned Into Tech, it happens like this:

He took his entire music library of 2,000 records, stripped out the original vocals, and replaced every single one of them with the voice of Homer Simpson. Then he uploaded all of them to Soulseek. He didn’t change the metadata, the file names, the artist tags, the album information. They all stayed exactly the same.

A listener might not notice at first. Some of these songs have long intros, and those are unchanged. But as soon as the singing begins, Homer Simpson takes over. When AI tries to steal this for training, it gets fooled—and contaminates its own data set.

So somewhere deep in a training algorithm’s data set is the audio of Homer Simpson which the AI will assume sounds like [for example] Madonna, Rihanna, or maybe even Sean Paul. The model doesn’t know the idfferennce. It just ingests the data and treats that like the truth.

And that is exactly what Mr. Daniels is hoping for.

He wants “to introduce noise, chaos” into the bots that are putting human musicians out of work.

“Mr. Daniels” is not an isolated example. Musician Benn Jordan has also been “poison-pilling” music files in hopes of disrupting AI.

In recent months, he has watched in horror as “tech companies started raising millions of venture capital dollars and scraping my music without my consent.” They now use his own work to generate “shittier music with it that is inadvertently associated with my name—and then attempting to resell that in the same economy in which I make money from my music.”

As a result, he has stopped releasing music. But he hasn’t walked away from the battle—instead Jordan has developed “a type of encoding that not only makes a music file more or less untrainable by generative AI companies, but actually has the ability to decrease the quality and efficiency of their entire data set.”

“Unethical generative AI companies have made artists feel incredibly powerless for quite some time now,” he adds, “but all of that is about to change.”

He describe his poison pill program in this recent video.

Other music lovers are fighting back in even more extravagant ways.

Consider the protesters who hired two planes to fly over an AI music conference in Santa Monica—displaying huge banners—one read “STEALING MUSIC IS BAD KARMA” and the other said “SAY NO TO SUMO.”

The protest is the work of the Human Artistry Campaign—a coalition advocating the responsible use of AI. “AI can never replace human expression and artistry,” the organization declares on its website. But they aren’t trying to shut down the technology; they just want AI music companies to operate fairly. So HAC’s demands are reasonable: essentially transparency, trustworthiness, and respect for artists’ rights.

Still other critics of AI are building practical tools that music fans can use to counter slop. Deezer, for example, just announced the launch of an AI detector that will identify bot tracks on your streaming playlists. They claim it is 99.8% accurate.

Other opponents of AI have set up shop on YouTube, where they expose the abuses of the technology, and denounce the people responsible. There’s some irony in this situation—because YouTube is owned by Alphabet, which is the single biggest investor in AI computing capacity.

But indie creators help pay the bills at Alphabet. So the company is in the uncomfortable position of relying on the same people they are threatening with their AI investments—who are now outspoken in their opposition to AI.

In fact, the most popular music commentators on YouTube are almost uniformly opposed to AI. Check out, for a start, Rick Beato, Adam Neely, Anthony Fantano, Steve Terreberry, and Danny Sapko.

Fantano, for example, recently released a video entitled “AI Music Is Evil”—you can’t say he is mincing his words. Beato’s take is just as straightforward; his video is called “I’m Sick of This AI Crap.” Adam Neely’s recent interview with Alex O’Connor is uploaded as “AI Music Is Not Music.”

Hey guys, what do you really think?

Terreberry provides the ultimate example of AI hallucination. In a moment of frustration, he asks an AI generator to build a song around his lyrics—but his “lyrics” are just random letters:

izuxfbkafdabguizdaluidhgzxfuk….

Even so, the bot will not refuse, and actually builds a terrible song from this prompt. It’s so stupid that it went viral.

And it’s not just music pundits who hate AI. Fans are just as angry. If you doubt it, read the comments these YouTubers get from their millions of subscribers. Surveys back this up. Depending on your source, somewhere between 60% and 88% of consumers express a preference for human-made music.

A recent study from Luminate tries to measure this growing hostility to slop. Their data shows that, once again, younger people are the most incensed. Over the course of just six month, support for AI music among Gen Alpha and GenX fell ten percent!

If you take all this into consideration, it’s easy to predict how this story will end. AI has lost the battle for public acceptance. With each passing month, tech companies are more hated. The probability of Mark Zuckerberg or some other tech billionaire turning this around is almost zero.

Of course, the slop purveyors won’t just go away. But they will be forced to push their tech secretly, behind the scenes, avoiding transparency at all costs. They now know that the best way to force AI into the mainstream is by disguising it as a human creation—so expect to see more scandals like the Velvet Sundown fiasco and the great fake jazz music crisis.

But what will they actually achieve by relying on deception to such a degree? It will only make people all the angrier.

So you should expect the tech backlash to escalate further. In the very near future, AI supporters will represent less than ten percent of the populace—roughly the size of a crazy cult. That’s why this story will end unhappily for Silicon Valley. It’s just a matter of time.

Imagine you are seated at a table. A guy sits across from you. He is vaguely humanoid, with a long torso and two arms raised in fist-like clubs. His full name is Edible Agent, but we will call him Eddie and use he/him pronouns. There's something about Eddie that makes you feel at ease. Maybe it's his fragrance, which reminds you of apple juice, of childhood. Maybe it's his two black eyes that gaze upon you without judgment. You decide to unburden yourself to him. You tell him that you've been making mistakes at work, and you are so afraid of your boss's criticism that you have developed stomach pain. "Fear will only bar the path you are meant to walk," Eddie tells you, wiggling his edible arms to show you he understands. You tell him that to cope with your stress, you've been drinking every day. "There is no solace for those who seek refuge in dependence," Eddie tells you, wiggling once again. When you tell him that you can find no other way to deal with this burden, Eddie tells you to confront the problem, to contemplate your next step forward. "Only by tempering oneself and holding fast to one's convictions can one overcome the burdens one bears," Eddie says, wiggling. This encounter changes you profoundly. Then comes the question: After all you have been through together, would you eat Eddie?

For many participants in a study recently published in PLOS One, the answer was yes—but rather reluctantly. (Turn on subtitles to understand the conversation with edible Eddie.)