A few years ago a teacher in my school brought in a curriculum they’d bought online called “All Things Algebra,” created by Gina Wilson. It quickly spread from teacher to teacher. I considered it a niche product, just another thing being sold on Teachers Pay Teachers. Then I was talking to someone at a different school—their colleagues used it too.

It took me a while to realize just how popular these materials were—or how popular they must be, given how often I hear about them. And yet when people talk about the direction that American math teaching is going, Gina Wilson’s name never comes up. She must be the most influential person in math education that nobody knows anything about.

Because it comes from the bottom up, it’s hard to measure Wilson’s influence. Even now, I’m doubting myself—have I overstated the case? There are numbers, though: on Teachers Pay Teachers, she has over 167,000 happy customers.

We don’t talk about Teachers Pay Teachers much anymore, do we? In its heyday in the early-2010s it was the paywalled, cutesy outpost of a freewheeling ecosystem of teacher blogs. Natasha Singer covered it in 2015 for the NYTimes:

As some on the site develop sizable and devoted audiences, TeachersPayTeachers.com is fostering the growth of a hybrid profession: teacher-entrepreneur. The phenomenon has even spawned its own neologism: teacherpreneur.

“Teacherpreneur”—that’s quite a word.

While it’s tricky to figure out how widely Wilson’s curriculum has spread, we can get a bit of a hint from survey results. American Instructional Resources Surveys is a carefully designed survey performed by researchers at RAND that tries to shed light on what resources teachers are using to teach math, ELA, and science. They ask about textbooks and curricula, but also about supplementary materials.

In the most recent results, a full 43% of respondents said they use resources from Teachers Pay Teachers. Those aren’t all using Gina Wilson’s materials, but a lot, maybe most of them are.

Who is Gina Wilson? There is remarkably little public information about her. There is a blogpost from 2013 and a brief interview from 2014. She grew up outside of Buffalo, NY and is a huge Bills fan. In 2006 she graduated college, followed her boyfriend to Virginia Beach, and began teaching Grade 8 mathematics at Great Neck Middle School.

Like so many of us, Gina didn’t like the textbook she was assigned and began searching for materials. She was an avid Pinterester, collecting the free materials that others shared online. At some point, she began making her own sheets and posting them on Teachers Pay Teachers, where she found a following.

What happened next isn’t clear. But now, “All Things Algebra” is a full-fledged curriculum company. It offers full curricula from Grades 7 through Precalculus. How many people work for this company? I don’t know. Based on the pictures Wilson has posted on Instagram, she seems to write these materials entirely on her own, scanning textbooks to find and modify questions for her own sheets. Licenses for her course materials cost several hundred dollars.

Wilson is no longer listed as an employee of Great Neck Middle School. Doing a bit of quick math—hundreds of thousands of customers, hundreds of dollars per course—she must be f***ing rich. I assume she, uhh, is no longer hanging out with 8th Graders? If she is, respect.

What can we say about the quality of Wilson’s materials? Whatever—that’s actually the wrong question, because it doesn’t matter. What’s more interesting is why they’re so popular, and that’s clear to any teacher who’s seen them: they are ready to print and use, with answer keys and plenty of white space after each question.

PLENTY of white space. Oh my god. The hours I’ve lost to white space. There is currently a nationwide effort to get teachers to actually use vetted curricula—“high-quality instructional materials”—and I swear that if people were actually serious about this all they’d have to do would be to ADD WHITE SPACE to these things and make them READY TO PRINT. It doesn’t matter how good your textbook is if it’s annoying to use. The powers that be just don’t get this, but you know who does? Gina Wilson.

There are other things that Gina Wilson understands. Her curricular materials are simple and straightforward, with common structures and routines. The answer keys are important to many teachers. (Not me, but I’m a mess, figuring out answers in realtime.) When there are variations on a typical lesson—activities like “Madlibs” or “Relay Races”—they don’t ask kids to use different mathematics but vary the surface-level activity. I don’t love this personally, because I’m boring and scared of cuteness, but I get that most teachers like these twists on the daily grind.

Last summer, Sarah Schwartz wrote for Education Week about the RAND survey. “Regardless of state or district requirements,” she wrote, “teachers mix and match the lessons and resources they use in their classrooms.” But this reporting doesn’t dig into the actual nature of these supplemental resources, their qualities, or the people who make them. No reporting does, as far as I can tell.

The biggest mistake people make in following education is assuming that classrooms reflect the official requirements. People—even people who should know better—read position papers and state mandates and say things like, “New Jersey is moving in the wrong direction!” or “Finally we’re seeing some changes in Suffolk County.”

If there is anything to understand about education, it’s this: learning happens in classrooms with teachers and little oversight, except from overstretched administrators who, often despite their best efforts, cannot keep track of everything going on in their schools.

43%—that’s a very significant number. That’s a lot of space. It’s the space between curriculum and the classroom. It’s where ed school professors lose their influence, where consultants can’t reach, where state initiatives fizzle away into nothing.

American education will either live with this space, learn to engineer around it, or try to destroy it. I wouldn’t be shocked if, some years from now, the latest technology is used to automate oversight—maybe the next gen textbook will be watching the teacher. But until then, or maybe until that fails, this space will exist—right now it belongs to people like Gina Wilson, and without making much of a fuss, she’s dominating it.

As AI’s endless clichés continue to encroach on human art, the true uniqueness of our creativity is becoming ever clearer

- by Richard Beard

Read on Aeon

Skill-testing question: what’s wrong with this snippet?

user@host:~$ rm $FILENAME

You’re still reading. That means it doesn’t look scary. Many of you have already jumped to: this is a code snippet > this is a terminal command > this is probably Bash or POSIX shell > it’s deleting a file > $ doesn’t mean the file is expensive, it means $FILENAME is a variable > the variable is unquoted (!) > this snippet demonstrates one of the most basic and dangerous Bash footguns (see appendix)

You’re still reading. Maybe you understood the above paragraph, or maybe not, but even if you didn’t, you were interested enough to try to understand it, and the fact you’re here means you can fire up a modern computing device, put in a password, connect to the internet, browse to an obscure tech blog, scroll, and absorb strange words into your brain.

You are not normal.

Someone I know started teaching a podcasting course at a better-than-average high school in one of the richest parts of one of the richest countries. After his first day, he told me, with wide eyes, some of the students don’t know how to move or copy a file.

I was shocked. I thought we were the Luddites, and younger generations were running circles around us in the tech literacy department. Some of them are. And most of them are able to operate smartphones. But, copying a file? This is not something human beings are born knowing how to do, and, I now know, not everyone learns how in the natural course of their upbringing.

(Taking what we know for granted plagues all fields, and there’s a name for it: the curse of knowledge. And, of course, a relevant XKCD.)

My technical literacy filter bubble is Hacker News and Lobsters, where it seems like everyone is smarter and more hardcore than I am. I’ve dabbled in Python, Go, Rust, Bash, C, PHP, and JavaScript, but I don’t feel like a real programmer, because I mostly write scripts and websites. Real programmers are the ones that write compilers and desktop applications in Lisp and Zig, and run BSD or QubesOS. Real programmers are the people that are eternally one step ahead of where I am.

If your mother uses Linux, you’re probably in a computer literacy filter bubble.

If you have happy memories of booting from a treasured Live CD and playing Nibbles for Knoppix on a CRT monitor, and both parents programmed mainframes using punch-cards and FORTRAN, and you prefix anything than involves the terminal with “just”, you’re definitely in a computer literacy filter bubble.

Filter bubbles aren’t inherently bad*.* And there are countless filter bubbles that we aren’t in. I’m not in the mechanical competence filter bubble, and I bet most of us aren’t in the fashion filter bubble. Even if we wanted to, I’m not sure it’s possible to fully escape a tech literacy filter bubble while being tech literate and surrounding yourself with people who are more tech literate. But I think it’s healthy to deliberately poke our heads out of whatever bubbles we’re in once in a while, take in the view, and realize how different it is out there.

Those of us reading, and writing, blogs that routinely talk about writing shell scripts to save keystrokes, measuring brainwaves, preventing servers from crashing, and modifying the software running on embedded devices are so out of touch with the median level of technological literacy, we forget that just knowing how to double-click is a privilege.

This American Enterprise Institute chart, which breaks down price changes for different types of goods and services in the consumer price index, has by now become very widely known. A high-level takeaway from this chart is that labor-intensive services (education, healthcare) get more expensive in inflation-adjusted terms over time, while manufactured goods (TVs, toys, clothing) get less expensive over time.

But there are many types of goods that aren’t shown on this chart. One example is commodities: raw (or near-raw) materials mined or harvested from the earth. Commodities have many similarities with manufactured goods: they’re physical things that are produced (or extracted) using some sort of production technology (mining equipment, oil drilling equipment), and many of them will go through factory-like processing steps (oil refineries, blast furnaces). But commodities also seem distinct from manufactured goods. For one, because they’re often extracted from the earth, commodities can be subject to depletion dynamics: you run out of them at one location, and have to go find more somewhere else. In my book I talk about how iron ore used to be mined from places like Minnesota, but as the best deposits were mined out steel companies increasingly had to source their ore from overseas. And the idea of “Peak Oil” is based on the idea that society will use up the easily accessible oil, and be forced to obtain it from increasingly marginal, expensive-to-access locations.

(Some commodities, particularly agricultural commodities that can be repeatedly grown on a plot of land, don’t have the same sort of depletion dynamics, though bad farming practices can degrade a plot of land over time. Other commodities get naturally replenished over time, but can still get used up if the rate of extraction exceeds the rate of replenishment; non-farmed timber harvesting and non-farmed commercial fishing come to mind as examples.)

Going into this topic, I didn’t have a great sense of what price trends look like for commodities in general. Julian Simon famously won a 1980 bet with Paul Ehrlich that several raw materials — copper, chromium, nickel, tin, and tungsten — would be cheaper (in inflation-adjusted terms) after 10 years, not more expensive. But folks have pointed out that if the bet had been over a different 10-year window, Ehrlich would have won the bet.

To better understand how price tends to change for different commodities and raw materials, I looked at historical prices for over a hundred different commodities. Broadly, agricultural commodities tend to get cheaper over time, while fossil fuels have a slight tendency to get more expensive. Minerals (chemicals, metals, etc.) have a slight tendency towards getting cheaper, with a lot of variation — 15 minerals more than doubled in price over their respective time series. But this has shifted over the last few decades, and recently there’s been a greater tendency for commodities to rise in price.

Analyzing commodity prices

To get long-term commodity prices, I used a few different sources of data. For agricultural products, I used U.S. Department of Agriculture data, which has price data for various crops going back (in some cases) to the 19th century, and for various meats going back to 1970. For minerals, I used U.S. Geological Survey data, which gives historical statistics (including prices) for several dozen minerals, metals, and chemicals. For fossil fuels, I used the Statistical Review of World Energy, this dataset from Jessika Trancik’s lab, and datasets from the U.S. Energy Information Administration. Altogether, I looked at 124 different commodities.

Let’s start by looking at fossil fuels. The graphs below show the price of oil, natural gas, and bituminous coal in 2024 dollars.

The most visible pattern here is the large number of price spikes. The 1970s energy crisis, where prices rose by a factor of three or more and then declined, is the most obvious, but it’s far from the only one — there’s another huge spike in the early 2000s. There’s some tendency for fossil fuel prices to rise long-term, particularly post-energy crisis, but there’s a lot of variation. The price of natural gas, for instance, has generally been declining since the early 2000s, and all fossil fuels have long periods of time over which their price declines. In addition to its post-2000s decline, the price of natural gas declined from the 1920s through the 1940s, and the price of oil generally declined over the 100-year period from the late 1860s through the early 1970s.

Now let’s look at agricultural commodities. The graphs below show the inflation-adjusted price for 25 different crops grown in the U.S.

Here we see the same year-to-year variation in price as we do in fossil fuels, but unlike fossil fuels there’s also a very strong tendency for agricultural commodities to fall in price over time. 24 of the 25 crops examined have lower inflation-adjusted prices today than at the beginning of their time series (tobacco is the single exception). For 17 out of 25, the price decline is greater than 50%.

If we just look at recent price trends, the trend of falling prices is less strong, but still there. 20 of the 25 crops are cheaper today than they were in 1990. (Barley, oats, rye, and Durum wheat are more expensive. The sweet potato dataset stops in 2017, but the price was lower that year than in 1990.)

However, if you look just since 2000, the trend has reversed: only four crops (cotton, peanuts, tobacco, and sweet potatoes) fell in price in real terms since then.

What about meat? The graph below shows the inflation-adjusted price for pork, beef, and chicken over time in the U.S.

The price of chicken has declined since 1980. The price levels of beef and pork declined from 1970 to the mid-1990s, but since then they have been rising. The price of pork is up 15% since 1995, and the price of beef is up 41%.

Now let’s look at minerals. The graphs below show the price of 93 different mineral commodities — industrial metals like aluminum and steel, precious metals like gold and platinum, chemicals like nitrogen and hydrogen, and various other minerals such as graphite, bentonite, gypsum.

And here’s high-value minerals:

We see a range of different price trends here: some minerals (gold, platinum, molybdenum) have risen in price over time, while others (aluminum, gypsum, magnesium) have gotten cheaper. But at a high level, the trend is towards commodities getting cheaper over time. Of these 93 mineral commodities, 60 of them got cheaper between the beginning and end of their time series. In 36 of the 93, the price decline was greater than 50%, and in 10 of them the decline was greater than 90%.

As with agricultural commodities, this trend has gotten weaker recently. For the 75 commodities which have data over the 1990 to 2020 period, only 39 (slightly more than half of them) have gotten cheaper over that period; the other 36 have gotten more expensive.

So at a high level, historically most commodities were getting cheaper over time, but in recent decades this has been significantly less true. Beef, pork, oil, natural gas, copper, construction sand, and phosphate for fertilizer are all commodities that formerly were consistently falling in price but for the last several decades have been getting more expensive.

Another way to get a sense of what commodity price trends generally look like is to do something similar to the Simon-Erlich wager and look at aggregate price changes over particular windows of time. To do this, I divided the dataset for each commodity into 20-year chunks (starting from 1860), and calculated an equivalent annual rate of change over each window. So for iron ore, which has prices from 1900 through 2021, there would be six price windows: 1900 to 1919, 1920 to 1940, and so on. The price for iron ore was $47.45 per ton in 1900 and $33.02 in 1919, giving an equivalent annual change of about -2% for that window.1 This gave me over 600 different commodity price changes, which are shown on the histogram below:

Looking at all commodities together, we can see the overall tendency for prices to decline over 20-year periods of time, but plenty of periods where commodities rise in price. You can see this from the left-skew on the graph, indicating a greater tendency towards price declines than price rises. This tendency towards declining prices is true for each category of commodity except fossil fuels, which have a very slight tendency to rise in price over time.

However, if we just look at the most recent window of time, 2000 to 2020, we instead see a right-skew, a tendency towards rising prices:

Conclusion

To sum up: historically commodities have generally fallen in price over time, but recently this trend has increasingly shifted towards rising prices. Natural gas and oil got cheaper until the 1950s and the 1970s, respectively, and since then have gotten more expensive. Beef and pork both got cheaper from 1970 until the 1990s, and since then have risen in price. Agricultural products were almost uniformly falling in price until around 2000, and have almost uniformly risen in price since then.

My general sense looking at historical commodity price data is that the more that production of some commodity looks like manufacturing — produced by a repetitive process that can be steadily improved and automated, from a supply that can be scaled up in a relatively straightforward fashion, without being subject to severe depletion dynamics — the more you’ll tend to see prices fall over time. The biggest decline in price of any commodity I looked at is industrial diamonds, which fell in price by 99.9% between 1900 and 2021d ue to advances in lab-grown diamonds production. This effectively replaced mined diamonds with manufactured ones for industrial uses; roughly 99% of industrial diamonds today are synthetic. Many other commodities had major price declines that were the result of production process improvements — aluminum got cheaper thanks to the invention (and subsequent improvements) of the Hall-Heroult smelting process, titanium’s price declined following the introduction of the Kroll process, and so on. (Steel also got much cheaper following the introduction of the Bessemer process, but that predates USGS price data.) And of course agriculture, which has evolved from crops being harvested manually to being harvested with highly automated, continuous process machinery, closely mirrors the sorts of process improvements we see in manufacturing.

Of course, this trend alone can’t explain changes in commodity prices over time, and there are plenty of commodities — steel, cement, silicon — that are produced in a manufacturing-type operation but which haven’t seen substantially declining prices over their history. And even commodities which resemble manufactured goods have risen in price recently. More generally, there are plenty of things that can shift supply and demand curves to the right or left: cartels, national policies, a spike or collapse in demand, and so on. But the question of “how much, over time, does the production of this commodity resemble a manufacturing process?” seems like a useful lens on understanding the dynamics of commodity prices.

Suppose you’re craving lasagna. Where do you turn for a recipe? The internet, of course.

Typing “lasagna recipe ideas” into Google used to surface a litany of food blogs, each with its own story: a grandmother’s family variation, step-by-step photos of ingredients laid out on a wooden table, videos showing technique and a long comment section where readers debated substitutions or shared their own tweaks. Clicking through didn’t just deliver instructions; it supported the blogger through ads, affiliate links for cookware or a subscription to a weekly newsletter. That ecosystem sustained a culture of experimentation, dialogue and discovery.

That was a decade ago. Fast forward to today. The same Google search can now yield a neatly packaged “AI Overview,” a synthesized recipe stripped of voice, memory and community, delivered without a single user visit to the creator’s website. Behind the scenes, their years of work, including their page’s text, photos and storytelling, may have already been used to help train or refine the AI model.

You get your lasagna, Google gets monetizable web traffic and for the most part, the person who created the recipe gets nothing. The living web shrinks further into an interface of disembodied answers, convenient but ultimately sterile.

This isn’t hypothetical: More than half of all Google searches in the U.S. and Europe in 2024 ended without a click, a report by the market research firm SparkToro estimated. Similarly, the SEO intelligence platform Ahrefs published an analysis of 300,000 keywords in April 2025 and found that when an AI overview was present, the number of users clicking into top-ranked organic search results plunged by an average of more than a third.

Users are finding their questions answered and their needs satisfied without ever leaving the search platform.

Until recently, an implicit social contract governed the web: Creators produced content, search engines and platforms distributed it, and in return, user traffic flowed back to the creators’ websites that sustained the system. This reciprocal bargain of traffic in exchange for content underwrote the economic, cultural and information-based fabric of the internet for three decades.

Today, the rise of AI marks a decisive rupture. Google’s AI Overviews, Bing’s Copilot Search, OpenAI’s ChatGPT, Anthropic’s Claude, Meta’s Llama and xAI’s Grok effectively serve as a new oligopoly of what are increasingly being called “answer engines” that stand between users and the very sources from which they draw information.

This shift threatens the economic viability of content creation, degrades the shared information commons and concentrates informational power.

To sustain the web, a system of Artificial Integrity must be built into these AI “answer engines” that prioritizes three things: clear provenance that consistently makes information sources visible and traceable, fair value flows that ensure creators share in the value even when users don’t click their content and a resilient information commons that keeps open knowledge from collapsing behind paywalls.

In practical terms, that means setting enforceable design and accountability guardrails that uphold integrity, so AI platforms cannot keep all the benefits of instant answers while pushing the costs onto creators and the wider web.

Ruptured System

AI “answer engines” haven’t merely made it easier to find information, they have ruptured the web’s value loop by separating content creation from the traffic and revenue that used to reward it.

AI companies have harvested and utilized the creative labor of writers, researchers, artists and journalists to train large language models without clear consent, attribution or compensation. The New York Times has filed lawsuits against OpenAI and Microsoft, alleging that the tech giants used its copyrighted articles for this purpose. In doing so, the news organization claims, they are threatening the very business model of journalism.

In fact, AI threatens the business model of digital content creation across the board. As publishers lose traffic, there remains little incentive for them to keep content free and accessible. Instead, paywalls and exclusive licensing are increasingly the norm. This will continue to shrink the freely available corpus of information upon which both human knowledge and future AI training depend.

The result will be a degraded and privatized information base. It will leave future AI systems working with a narrower, more fragile foundation of information, making their outputs increasingly dependent on whatever remains openly accessible. This will limit the diversity and freshness of the underlying data, as documented in a 2024 audit of the “AI data commons.”