Returning from a trip to New Mexico to explore some Puebloan ruins, I picked up this beautiful chunk of labradorite in the town of Quartzsite. This mineral creates an eerie blue shimmer in the sunlight: a phenomenon called ‘labradorescence’. Reading up on it, I discovered it’s a form of feldspar.
60% of the Earth’s crust is feldspar, and I know so little about this stuff! It turns out there are 3 fundamental kinds:
• orthoclase is potassium aluminosilicate
• albite is sodium aluminosilicate
• anorthite is calcium aluminosilicate
Then there are lots of feldspars that contain different amounts of potassium, sodium and calcium. We get a triangle of feldspars with orthoclase, albite and anorthite at the corners. You can find labradorite on this triangle:
But not all points in this triangle are possible kinds of feldspar! There’s a big region called the ‘miscibility gap’, where as you cool the molten mix it separates out. Apparently this is because the radius of calcium is too much bigger than that of potassium for them to get along. Sodium has an intermediate radius so it gets along with either calcium or potassium.
And there are also subtler issues. When you cool down the feldspar called labradorite, it separates out a little, forming tiny layers of two different kinds of stuff. When the thickness of these layers is the wavelength of visible light, you get a weird optical effect: labradorescence! You really need a movie to see the strange shimmer as you turn a piece of labradorite in the sunlight.
In fact there are 3 kinds of feldspar that separate out slightly as they cool and harden, forming thin alternating layers of two substances:
• The ‘peristerite gap’ produces layers in feldspars with 2-16% anorthite and the rest albite: these layers create the beauty of moonstone!
• The ‘Bøggild gap’ produces layers in feldspars with 47-58% anorthite and the rest albite: these are labradorites!
• The ‘Huttenlocher gap’ produces layers in feldspars with 67-90% anorthite and the rest albite: these are called ‘bytownites’. For some reason these layers do not seem to produce an interesting visual effect. Maybe their thickness is too far from the wavelength of visible light.
All these gaps are ‘miscibility gaps’: that is, feldspars with these concentrations of anorthite and albite are unstable: they want to separate out. That’s why they form layers.
The physics and math of all this stuff is fascinating. Crystals try to do whatever it takes to minimize free energy, which is energy minus entropy times temperature. That’s why many feldspars have different high- and low-temperature forms. But sometimes when molten rock cools quickly, it doesn’t have time to reach its free energy minimizing state.
For feldspar all of these issues are complex, because feldspar crystals are complicated structures:
Aluminum and silicon have to be distributed among the corners of the tetrahedra here, and there are various ways to do this. The distribution is determined by the relative amounts of potassium, sodium and calcium, which are the white balls. The distribution of aluminum and silicon in turn controls the symmetry of the crystal, which can be either ‘monoclinic’ or the less symmetrical ‘triclinic’.
The picture here shows the difference between monoclinic and triclinic crystals:
But the picture doesn’t fully capture the symmetry group of an actual crystal—because there’s more to a crystal than just a shape of a parallelipiped! There may be the same atoms at all corners of the parallelipiped, or not, and there may also other atoms not on the corners.
where T ≅ ℤ³ is the group of translational symmetries and P is the group of symmetries that fix a point, called the ‘point group’. This sequence may or may not split! It splits iff G is a semidirect product of P and T.
For a triclinic crystal, there are only two possible space groups G, and both are semidirect products. P is either trivial or ℤ/2, acting by negation.
For a monoclinic crystal, there are 3 choices of the point group P as a subgroup of O(3):
• P = ℤ/2 (a single 2-fold rotation)
• P = ℤ/2 (a single reflection)
• P = ℤ/2 × ℤ/2 (generated by a 2-fold rotation and inversion
(𝑥,𝑦,𝑧) ↦ -(𝑥,𝑦,𝑧): their product is a reflection).
For each choice of P there are 2 fundamentally different choices of lattice T ≅ ℤ³ it can act on. One is made up of copies of the parallelipiped I showed you. The other is twice as dense; then we call the lattice ‘base-centered monoclinic’:
So, we get 3 × 2 = 6 space groups G that are semidirect products.
But there are 7 other non-split extensions! These other 7 give nontrivial elements of the cohomology group H²(P, T). It’s not obvious that there are just 7 options. Thus, the hardest part of the classification of all 13 monoclinic space groups is essentially the computation of H²(P, ℤ³) for all 6 choices of groups P and their actions on ℤ³.
I knew that cohomology rocks. But it turns out cohomology helps classify rocks!
Now, which of these various groups are symmetry groups of feldspars?
Apparently all the feldspars in the triangle have just two different symmetry groups:
• For the monoclinic feldspars (including sanidine, orthoclase, and high-temperature albite), the crystal has a 2-fold rotational symmetry, a mirror plane, and inversion symmetry
(𝑥,𝑦,𝑧) ↦ -(𝑥,𝑦,𝑧).
The point group is the Klein four-group ℤ/2 × ℤ/2. The lattice is base-centered monoclinic, so there’s an extra translational symmetry shifting by half a cell diagonally across one face of the parallelipiped.
• For the triclinic feldspars (including microcline, low-temperature albite, and anorthite), the only symmetry beyond translation is inversion. So the point group is just ℤ/2. And there are no extra generators of translation symmetry beyond the three edges of the parallelipiped.
Alas, each of these space groups G is the semidirect product of their point group P and their translation symmetry group T ≅ ℤ³. So, no interesting cohomology classes show up!
Nontrivial cohomology classes show up only in crystals where you can’t cleanly separate the translations from the symmetries that fix a point of the crystal. This happens when your crystal has ‘screw axes’ or ‘glide planes’. A screw axis is an axis where you’ve got a symmetry of translating along that axis, but only if you also rotate around it:
A glide plane is a plane where you’ve got a symmetry of translating along that plane, but only if you also reflect across it:
But wait! There’s a rarer kind of feldspar made with barium. It’s called celsian, after Anders Celsius, the guy who invented the temperature scale. Chemically it’s barium aluminosilicate. And its crystal structure has both screw axes and glide planes! So its space group G is not a semidirect product! It’s an extension of ℤ³ by the point group P = ℤ/2 × ℤ/2 that gives a nonzero element of H²(P, ℤ³). See the end of this post for some details.
All this is lots of fun to me: you start with a pretty rock, and before long you’re doing group cohomology. But the classification of symmetry groups is just the start. For mathematical physicists, one fun thing about feldspars is their phase transitions, especially the symmetry-breaking phase transition from the more symmetrical monoclinic feldspars to the less symmetrical triclinic ones! There’s a whole body of work—by Salje, Carpenter, and others—applying Landau’s theory of symmetry-breaking phase transitions to map out the space of different possible feldspar crystals! Here’s one way to get started:
Even if you don’t particularly care about feldspars, there are a lot of good general principles of physics to learn here!
Details
Let me sketch out why barium aluminosilicate, or celsian, has a space group G that’s described by a non-split short exact sequence:
0 → T → G → P → 1
Its point group is P = {e, r, m, i} ≅ ℤ/2 × ℤ/2, where we can take r to be a 180° rotation about the y axis and m to be a reflection that negates the y coordinate, so that i = rm is inversion. In coordinates:
r acts as (x, y, z) ↦ (−x, y, −z)
m acts as (x, y, z) ↦ (x, −y, z)
i acts as (x, y, z) ↦ (−x, −y, −z)
We can take the translation lattice T ≅ ℤ³ to be the lattice generated by
f₁ = (1,0,0), f₂ = (0,1,0), f₃ = (½,½,½)
Note that (0,0,½) is not in T.
To compute the 2-cocycle we need a set-theoretic section s: P → G. We choose
s(e) = identity
s(m) = a glide reflection: (x, y, z) → (x, −y, z + ½)
s(i) = inversion: (x, y, z) → (−x, −y, −z)
s(r) = s(i)·s(m): (x, y, z) → (−x, y, −z + ½)
As usual, the 2-cocycle c: P2 → G is defined by
c(g,h) = s(g)·s(h)·s(gh)⁻¹
The interesting value is c(m, m): the glide composed with itself gives (x, y, z) → (x, −y, z+½) → (x, y, z+1), so s(m)² = translation by (0, 0, 1), while s(m²) = s(e) is the identity. Thus c(m, m) = (0, 0, 1). The other values are trivial: c(i, i) = 0, c(r, r) = 0.
Now, is this cocycle nontrivial in H²(P, T)? It would be trivial if we could find a different section that makes the cocycle zero—that is, find a function b: P → T such that replacing s(g) with s'(g) = s(g) + b(g) makes
c'(g,h) = s'(g)·s'(h)·s'(gh)⁻¹
be the identity for all g,h. I will spare you the calculation proving this is impossible. The idea is simply this: the reflection m squares to the identity in the point group, but no matter how we choose b, s'(m) is a glide reflection, so it squares to a nontrivial translation. On the other hand, s'(m2) is trivial since m2 is, so
“This (tool / workflow / service / slot machine) is slower than me doing it manually.
Therefore, it's not worth using.”
These people are missing the point. Speed is easy to measure - that’s great. But focusing on speed overlooks the importance of subjective effort and mental load.
Let's talk about grocery stores, naval signaling flags, and the value beyond time saved.
The grocery store self-checkout
In the grocery store, do you choose a human cashier or the self-checkout machine?
People who prefer self-checkout often believe that it's faster. But in my highly-scientific study (loitering in the soup aisle with a stopwatch, n=24), the fastest self-checkout user was only equal in speed to the average cashier in scanning items. Once you add in the time to bag items and pay (not to mention "unexpected item in bagging area"), most people have no chance to outpace a human cashier.
The true value of the self-checkout is to offload social effort, the weight of interaction. Arthur Schopenhauer lived too early to scan his own groceries, but he did write, "A man can be himself only so long as he is alone... for it is only when he is alone that he is really free."
It's flag semaphore for the letter "U", of course. One weekend, I was tired of staring at the lookup table for flag semaphore (a common cipher used in puzzle hunts), so I made an interactive graphical tool to help me decode it.
It worked great and I felt that it was way faster, just like our self-checkout users above. But after a highly-scientific evaluation (decoding four phrases from my friend Johnny), I was surprised to learn the decoder was only equal in speed to the lookup table.
The true value of the semaphore decoder is to offload cognitive effort. Instead of burning mental energy trying to match a shape in a lookup table, I can mechanically use my tool to grind through the rote decoding process - no faster, but still easier.
That means I'm free to focus my efforts on more interesting, fun, and challenging aspects of the puzzle. I keep more energy for the next puzzles in the hunt.
Lighter burdens make your journey feel faster
Via my third and final highly-scientific study (my personal vibes), I've come to believe we fixate on speed and time measures for two reasons:
But even if a new approach is equally slow or even slower, the value of reducing effort is real.
I've previously written a bit on the history of the command bar/palette seen in apps like VS Code, Notion, and Slack. That’s a UI pattern that is actually slower than dedicated keyboard shortcuts, but it provides a better user experience by providing better discoverability and reducing cognitive load.
The self-checkout is slower, but I can relax a little. The semaphore decoder isn't any faster, but I feel less mentally tired. And the command bar is slower, but I can always find what I was looking for.
Sure, if all things are equal, I prefer faster. Who wouldn’t? But if you only prioritize hard numbers and squeezing out every moment of savings, you are going to miss the opportunity to make your effort lighter - and relax for the same result.
I like the tools that make my work lighter, not just faster.
A few years ago, some sort of a bug at my work caused all of the timestamps appear as “54 years ago,” a seemingly arbitrary date. It took me a bit to realize: “Wait, you know what year was 54 years ago? 1970!” “Why is 1970 important?” asked another designer. I explained that by convention, Linux time counts up from Jan 1, 1970 – and so if the time “value” is zero or unavailable, as it was because of the bug, it would be rendered not as an error, but as that specific day long ago.
Computing is filled with all sorts of arbitrary numbers like these. The most famous one was Y2K (99 + 1 = 00 if you only allocate two digits), Pac-Man’s kill screen was number 256, people still bring up the infamous and likely non-existent “640 kilobytes should be enough for everybody” quote, and the Deep Impact space probe died a lonely and undignified death after its timers overflowed the two pairs of bytes given to them.
Here’s a new magic number to remember: macOS Tahoe has, for a while at least, a kill screen of its own – after 49 days, 17 hours, 2 minutes, and 47 seconds (or, 4,294,967,295 milliseconds), one of its time counters overflows and no new network connections can be made, rendering the machine rather useless. The only solution is a reboot. Talk about a deadline!
Wikipedia has a nice list of other time storage bugs. The next big one? The problem of the year 2038. The technical fix, as always, is to give the numbers a bit more room to breathe. This is, in a way, kicking the can down the road, but that might be okay since the road is rather long:
Modern systems and software updates address this problem by using signed 64-bit integers, which will take 292 billion years to overflow—approximately 21 times the estimated age of the universe.
There are two articles out this week about my tech-free experiment, one in The Atlantic (gift link) and one in Chalkbeat. I thought the articles did a good job capturing my experiment so I won’t retread everything. Go read the articles, or if you want to see my writing on technology you can check it out here, and here, and here. I do have a few quick reflections on some responses I saw to the articles.
I’ve Moved On
It’s funny these articles came out now because I did the experiment in January. I feel like I’ve moved on. It’s not an experiment anymore. It’s just the way I teach. I radically reduced technology use in my class, I’m happy with that, and I’m thinking about a lot of other aspects of my teaching these days. I emphasize this because there’s a growing movement advocating for less technology in schools. I think that’s a good thing! But classroom technology is just one of many, many things that matter for student learning. Some of the big claims I’ve seen about technology use harming learning don’t seem in line with the evidence. Let’s reduce classroom technology use, sure, but let’s be realistic about the impact that will have on student achievement, and also work on the dozens of other things that can help students learn.
Just a Tool
A few responses said something along the lines of, “Sure, technology can be overused, but computers are a tool just like any other. Why is it such a big deal not to use student-facing technology at all? Why not only use it when it benefits learning?”
Before my experiment, I would have said that I’m someone who doesn’t use technology very much. I’d estimate we used Chromebooks about 20% of class time. Going cold turkey for a month was a helpful way to see that even that 20% was way too much. I’m also not completely zero-tech now. We’ve used Chromebooks twice in the last three months, both for narrow practical purposes.1 I’d recommend teachers give a tech-free experiment a try, just to see how they feel about it.
A related point here is that schools spend a ton of time, energy, and money on student technology. Something like 90% of US schools provide one-to-one devices to students. On Monday, when I asked students to pull out their Chromebooks, one looked like this:
This is the type of stuff teachers and other staff deal with when students carry Chromebooks around all day every day. The practical question here is whether schools should be providing one-to-one devices. Maybe schools should have a computer lab or shared Chromebook cart instead.
Assessment
A final response I saw a few times was surprise that I was giving quizzes and tests on Chromebooks before my tech-free experiment. I get it. That was dumb. In retrospect, it seems obvious students should be assessed on paper. I started giving assessments online during covid for practical reasons, and kindof just never thought twice about it again. They were easy to grade and there was no paper to keep track of for absent students. Students take most high-stakes assessments online now, so there’s a common logic in schools that students should practice taking other assessments online. There’s also a bit of cachet that comes with doing things with technology. I remember one time a few years ago the superintendent was doing a classroom walkthrough and I was giving a quiz. She complimented how nifty one of the interactive online questions was. They can be pretty nifty! That doesn’t mean it was good teaching, but there’s an assumption in education that using technology is innovative or whatever. This is a long-winded way of saying: trying a tech-free experiment can help teachers look at their practices from a new perspective, and maybe realize that they’re using technology for the wrong reasons.
There’s a lot of talk these days about how students are addicted to their phones. I think teachers can just as easily get addicted to classroom technology. The classroom tech I used before definitely made my life a bit easier. I work a bit harder now. That’s fine with me. I think it’s worth the trade. Students work a bit harder too! Those are exactly the types of conversations I hope schools start having about their technology use.
If you’re curious what I’ve used Chromebooks for: Once was about 15 minutes for this Desmos activity that I find helpful for teaching the triangle inequality theorem. Shoutout to Desmos, if you’re going to use technology in math class it’s the best tool out there. The second time happened to be on Monday — my students took the computer-based state test on Tuesday/Wednesday/Thursday this week, so we spent the last 10 minutes of class on Monday practicing typing math notation on computers.
While high school GPAs have been gliding upwards for years, college admissions officers have relied on Advanced Placement (AP) exams as a more stable, rigorous measure of college readiness. That confidence is now misplaced—at least for most of the exams that dominate the AP landscape.
The College Board has phased in a new scoring system that has inflated student results on nine of the most frequently taken AP exams. The share of students receiving the top score of 5 on these exams has jumped by an average of 61 percent in just four years. The share receiving a passing score (3 or higher) has risen by 37 percent.
Some less common AP exams still appear to function as reliable indicators of high academic achievement. But for the most popular exams, high school counselors and college admissions committees must go beyond a quick glance at the AP scores listed on an application. They now need to look closely at which AP exams a student took, and in which years.
Trevor Packer, the senior vice president in charge of AP programs, denies that any score inflation has occurred. He has described the claim that AP is being “dumbed down” as “entirely false.” This essay explains how the scoring system has changed, demonstrates that inflation has occurred, and shows why the official denials are misleading.
Why AP Matters
High performance on AP exams is an important way students signal readiness for rigorous college work:
Scores of 5 on multiple tests serve as a positive signal that a student is prepared for admission to Ivy League and other highly selective institutions.
Many selective but non-elite colleges award course credit or waive introductory course requirements for scores of 4 or 5.
Most non-selective colleges grant credit for a passing score of 3 or higher.
The financial stakes are high. By substituting a high school AP course for a college course, students can reduce college costs and shorten their time to earning a degree. Reflecting this, more than 1.3 million high school students in 2025 paid a $99 fee for each of over 4.8 million AP exams.
Given these stakes, the integrity of AP exams depends on a scoring system that is stable across subjects and from year to year.
How AP Scoring Used to Work
Until 2022, the College Board used a relatively consistent procedure to set score distributions:
Each AP exam was reviewed every 5–10 years by a panel of approximately 10 to 18 experienced college professors and high school teachers.
These experts had deep subject-matter knowledge and a clear sense of what level of performance justified advanced placement in college.
They determined what share of test takers should receive each of the five AP scores (1 through 5).
Under this system, the distribution of students awarded scores of 5, 4, 3, and so on was anchored to the standards of a carefully selected expert group and remained fairly stable over time.
The Shift to a New Scoring System
After 2021, the College Board began phasing in a different approach for nine of its most popular exams:
English Language and Composition
U.S. History
English Literature and Composition
World History
U.S. Government and Politics
Psychology
Biology
Human Geography
Chemistry
Less commonly taken exams—such as Music Theory, Art History, Japanese, Italian, and Physics (Electricity and Magnetism)— continue to be scored under the traditional expert-judgment system.
What Happened to the Scores?
Under the new system, performance on the nine popular exams suddenly “improved” in ways that are historically unprecedented:
Top score (5): The share of students earning a 5 increased from about 10 percent in 2021 to 17 percent in 2025, on average—a 61 percent increase. Under the old system, the share of 5s awarded in these subjects, on average, hardly changed over the previous six years.
Top two scores (4 or 5): In 2021, just 28 percent of test takers received a 4 or 5 on these nine exams. By 2025, that had jumped to 45 percent, a gain of 17 percentage points—or a roughly 63 percent increase.
Passing scores (3 or higher): The share of students receiving a 3 or better rose from roughly 52 percent to 71 percent over the same period, a 19 percentage-point increase—resulting in a 37 percent jump in passing rates.
Such large, rapid gains call for explanation.
Three Possible Explanations
Three broad explanations (or some combination of them) could account for this sudden surge in scores:
The test-taking pool became more selective. Perhaps weaker students stopped taking AP exams, leaving a stronger group of test takers.
This is easily rejected. Since 2021, the number of AP test takers has increased, not decreased. The pool has expanded rather than narrowed to a high-performing elite.
Teaching and learning improved dramatically. Perhaps teachers and students suddenly found far more effective ways to teach and learn AP material.
If students’ knowledge truly improved so dramatically, we would expect to see similar gains on other large-scale, independent tests. In fact, national and international data tell a different story:
NAEP (National Assessment of Educational Progress) scores in 8th-grade math, reading, and science were already slipping before Covid and have fallen sharply since. 12th-grade math and reading scores also declined between 2019 and 2024.
PISA (Programme for International Student Assessment) scores show stagnation in science and reading for U.S. 15-year-olds since 2015, and a decline in math.
These results provide no evidence of a sudden, broad-based leap in academic achievement. If anything, they point to stagnation and decline.
That leaves a third explanation.
The scoring system was relaxed. Perhaps a new evaluative approach altered the way tests were scored so that higher scores were given for the same level of performance. Let’s take a look.
Evidence-Based Standard Setting (EBSS): The New Method
After 2021, the College Board introduced what it calls “Evidence-Based Standard Setting” (EBSS) to determine score distributions on its most popular AP exams.
Under EBSS, the College Board consults hundreds of college instructors instead of relying on a small panel of carefully selected experts. These instructors are asked to recommend what proportion of students should receive each AP score.
In practice, the standards produced by this large, dispersed group are substantially lower than those set by the traditional expert panels.
The Impact of EBSS
With the implementation of EBSS, the share of passing scores rose sharply across the nine popular courses that that used it. The size of the increase varies by subject:
In English Literature, U.S. History, U.S. Government and Politics, and, the share of 4s and 5s rose by 24 percentage points or more (see Figure 1).
In Psychology and English Language and Composition, the increase was smaller but still substantial—about 9 or 10 percentage points.
In each of the nine subjects, EBSS is associated with higher scores and higher passing rates between 2021 and 2025.
The largest score increases on each exam within this period correspond to the specific year when EBSS was first applied.
These patterns are precisely what we would expect if the scoring standards had been relaxed.
Parsing the Official Denials
Trevor Packer insists there has been no “dumbing down” of AP exams, stating, “The exams themselves have not changed . . . Well-established equating processes ensure the difficulty of AP Exams remains consistent from year to year.”
This statement is technically correct but strategically framed. It emphasizes one piece of the puzzle (the difficulty of the test questions) while ignoring another (the conversion of raw test scores into AP scores).
Dumbing down does not require easier questions. It can be achieved just as effectively by changing how test scores are mapped onto the 1–5 scale—exactly what EBSS does.
According to one report of a public appearance, Packer acknowledged that the College Board aimed “to bring all exams to between a 60 and 80 percent success rate.” In 2025, the average passing rate on the nine EBSS exams was 71 percent, almost exactly the midpoint of that target range. EBSS appears to have been used to recruit scorers whose standards would produce the desired “success” rates.
Packer further claims that fluctuations in passing rates are driven by changes in student performance, pointing to recent declines in pass rates for AP Calculus BC, AP Statistics, AP Physics C: Mechanics, and AP Government and Politics courses. He neglects to highlight that:
All but one of these courses have not been subjected to EBSS.
For AP U.S. Government and Politics, the 2025 pass rate is only slightly below its 2024 level—after a 20 percentage-point increase following the adoption of EBSS.
The pattern is consistent: where EBSS is applied, scores rise substantially; where it is not, scores tend to reflect the stagnation or decline seen in broader national tests.
AP vs. IB and the Role of Marketing
To justify higher AP passing rates, Packer points to the International Baccalaureate (IB) program, where roughly 80 percent of candidates succeed. The comparison is misleading:
IB is an integrated two-year program, not a set of independent single-course exams.
Earning the IB diploma requires sustained performance across multiple subjects and assessments over time.
Nonetheless, the comparison reveals something important: the College Board is attentive to market positioning. If IB can boast an 80 percent “success” rate, AP’s passing rates must appear competitive to students, parents, schools, and policymakers.
Financial Incentives and Score Inflation
Market considerations are not incidental to the College Board. They are central to its operations:
In 2024, over 86 percent of College Board revenue came from fees and similar payments, including 48 percent from the basic AP exam fee.
In 2024, total revenues exceeded $1.17 billion, and the organization held reserves of over $2 billion.
Generous compensation at the top reinforces these incentives:
The CEO received $2.3 million in total compensation in 2024, comparable to the pay of the president of Stanford University, though Stanford’s operating budget is about ten times larger.
The second-in-command earned $1.5 million.
To sustain these revenues and salaries, the College Board must keep AP attractive to schools and students. Guaranteeing that more than two-thirds of test takers “succeed”—via relaxed scoring standards—serves that purpose well.
If this requires inflating AP scores, so be it. The more troubling question is why a senior vice president feels compelled to deny the inflation and to frame it instead as a story of scoring becoming “more precise.”
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Implications for Admissions Officers and Counselors
For college admissions officers, high school counselors, and policymakers, several implications follow:
AP scores are no longer directly comparable across subjects and years. A score of 4 in AP U.S. History today (post-EBSS) does not mean the same thing as a 4 in U.S. History before 2021, nor as a 4 in AP Music Theory (still scored under the old system).
The most popular exams are the most inflated. The very tests taken by the largest number of students—those that dominate application profiles—are the ones whose standards have been relaxed.
Context now matters critically. Evaluators should:
Note which AP courses and exams a student took.
Check the year(s) in which those exams were taken.
Recognize that high scores on EBSS-affected exams are far less informative than scores on exams that retained traditional standard setting.
AP exams, once a gold-standard external check on grade inflation, now vary in reliability. Without close attention to subject and year, admissions decisions risk being distorted by hidden inflation.
Conclusion
The College Board’s shift to Evidence-Based Standard Setting for its most popular AP exams has produced an unmistakable pattern of score inflation, even as broader measures of student achievement show stagnation or decline. Official statements that “the exams themselves have not changed” hide the central fact: the scoring system has changed, in ways that dramatically raise reported performance.
AP remains influential in college admissions and credit decisions, but its signals are no longer uniform or stable. Those who rely on AP scores must recognize that some exam results reflect not a surge in student learning but a quiet lowering of the bar.
Paul E. Peterson is the Henry Lee Shattuck Professor of Government and Director of the Program on Education Policy and Governance at Harvard University. He is also a Senior Fellow at the Hoover Institution, Stanford University.
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Tim Requarth| Longreads | April 9, 2026 | 18 minutes (5,003 words)
How does the brain decide what’s real? It’s a question most of us never have to ask. Our memories feel like records—imperfect, sure, but records nonetheless. We trust them to tell us where we’ve been, what we’ve done, who we are. But that trust rests on neural machinery we can’t access, reality-sorting processes that operate beneath conscious awareness.
My wife insists we once took a yoga class together, early in our relationship. She remembers the teacher vividly (a French acrobat, rainbow dreads, apparently quite a character), where we sat (to the left of the door), and the color of the yoga mats (teal). I insist she is misremembering: I have never been to a yoga class, even to this day. I scrolled back years through my phone’s location history once to settle it, but we’d started dating not long after the iPhone came out, and if the data ever existed, it was gone. The yoga story comes up every few years, but we never resolve it. It is probably unresolvable. As a neuroscientist, I know how these things happen—the encoding mishaps, the source confusion, the neuroscience of how two people can end up telling different stories about the same afternoon. This knowledge has never once brought us closer to agreeing.
I was thinking about this story when I heard something strange from a neighborhood friend of mine, Andrew Deutsch, who was using OpenAI’s Sora app. Sora, if you aren’t familiar, worked like this: You would record your face, say a few numbers, rotate your head left to right. Moments later, you would have an AI video replica of yourself, a self-deepfake, insertable into any scenario you can prompt the AI to produce. Scuba diving with SpongeBob. Dancing K-pop style in a futuristic cityscape. You could then share your videos with your friends and scroll through the videos of others, in what is often described as a “TikTok for deepfakes.” Sora hit one million downloads in only five days. Six months later, OpenAI shut it down, reportedly redirecting resources toward coding tools ahead of a planned IPO. Consider this, then, a eulogy for Sora, a technology with the lifespan of an off-Broadway flop that, in its brief and ignominious run, exposed a crack in human cognition that the next self-deepfake app will surely exploit.
I’d had an early invite to try it for weeks, but couldn’t quite bring myself to open it. Deutsch, on the other hand, had been using Sora heavily. He’s worked in animation and augmented reality for 20 years; he’s both interested in and not easily duped by tech. But he’d been using Sora to make AI videos of himself doing things he’s never done. And now he’s having trouble. Not with the videos. With his memory.
He created an AI-generated video of himself scaling Mount Rushmore and watched it several times. Then, a few weeks later, he was getting his dog ready for a walk. He felt a flicker of recollection, of that time he’d climbed Mount Rushmore. “I felt just this twitch of confusion about it. It felt like a memory, very faintly.”
Not a full memory, exactly. But not not a memory either.
A memory twitch like this might not sound alarming, especially considering the many dangers of deepfake technology like Sora. Within hours of its public release, users were generating videos of mass shootings, copyrighted characters promoting crypto scams, SpongeBob dressed as Hitler. Misinformation, slop, harassment. Those are real problems. But something subtler and eerier is going on with Deutsch. His minor but real neurological glitch is a sentinel signal that technologies like Sora are capable of interfering with some of the brain’s root processes. Things like autobiographical memories, which form the raw material of identity. Things like how the brain determines whether a thought is a memory based in reality, or not. Sora was just the first app that let you deepfake yourself; I suspect it won’t be the last. I wanted to understand what was happening in the brain—and what it means that a free app on your phone can now manufacture, in seconds, the kind of mental imagery the brain is least equipped to reject.
To get a sense of what might be going on in Deutsch’s brain, I called up Elizabeth Loftus, the psychologist who made “false memory” a household word. Her famous 1995 “lost in the mall” study convinced people they’d been lost in a shopping mall as children, using nothing more than a fabricated paragraph slipped in among real family memories. More recently, she teamed up with MIT’s Media Lab to show that AI-generated videos from AI-edited images could double false memory rates.
When I described what Deutsch had experienced, she wasn’t surprised. Exposure to AI-generated images or video, she said, could absolutely contaminate memory.
She was intrigued. Most deepfake research, including her recent work with MIT Media Lab, focuses on memories about other people or events. The concern is misinformation. You see an AI-altered image of a politician, and later you misremember what they did. At scale, chaos ensues. What Sora enabled was different: false memories about yourself. I’d first heard of Loftus’s work during a seminar on memory at Columbia, sitting in a cramped room at the Neurological Institute on 168th Street. The circumstances always seemed like edge cases—whether undergrads tricked by clever experimenters, or traumatized patients confused by leading questions. This was not a phenomenon I thought would be replicated by a short-form video AI slop app.
I wanted to understand what was happening in the brain—and what it means that a free app on your phone can now manufacture, in seconds, the kind of mental imagery the brain is least equipped to reject.
To understand why we’re so susceptible to false memories requires understanding that the brain doesn’t store memories the way a phone stores photos. When you live through something, your hippocampus— a deep brain structure vaguely shaped like a seahorse—encodes that experience by binding together its constituent pieces: what you saw, what you heard, where you were, how you felt. That bound-together pattern is the memory. Over hours and days, the hippocampus replays these patterns, perhaps while you sleep, gradually strengthening their hold in the cortex, in a process called consolidation. What makes these memories so unlike phone storage, and especially relevant here, is that recalling a memory means the brain must partially relive it. The brain recalls by reactivating some of the same sensory and spatial patterns that were present during the original experience. Your brain doesn’t access a stable, static stored memory of yourself at that summer picnic in the park; your brain recreates it by activating some of the same neural circuitry that fired when you were actually squinting in the sun, actually wiggling your toes in the warmed grass. During recall, it fires again, faintly.
The beauty of memory, not as a static storage bank but as a dynamic process of on-demand re-creation, is that it’s efficient. You can access a tremendous amount of information about your past without having to dedicate special storage space to your personal archive. But that efficiency comes with risks. Each time you replay and reconsolidate a memory, it can subtly change. Other things you’re thinking about during recall, how you feel while recalling it, other, similar memories that activate similar patterns of neurons, these can mix and mingle and, ultimately, change the reconsolidation of the original memory itself. And once changed, it doesn’t revert because there is no gold-standard stored version. There is only the latest replay. And because memories are, essentially, reactivations of specific patterns of sensory and other neural activity, that means that sensory patterns alone can get consolidated as memories. This is a false memory. And a false memory, once seeded, benefits from the same machinery as real ones. And the brain’s fact-checker, the prefrontal cortex, arrives late to the scene: the reactivation of sensory and other neural pathways is already underway, the memory reconstruction already in progress, before any evaluation of whether the memory is genuine even begins.
Applying any of this to a deepfake app is uncharted territory, but talking to Loftus, I started to see how false memory science might apply. The passage of time would likely be important. Initially, a person might remember creating a specific video, and the mind could reject the contents as false. But if the memory of creation fades while the contents persist, the pre-frontal fact-checking defenses begin to disappear. The false memory is more likely to feel real. False memories would probably strengthen with repeated exposure to the video—the illusory truth effect shows that repetition makes false claims feel truer, and while studies of false autobiographical memory have mostly involved active suggestion rather than passive viewing, those using multiple sessions consistently produced stronger effects. So false Sora memories would probably strengthen with repeated exposure to the video—essentially, Sora would be stimulating replay processes in the brain, helping to further consolidate the false memory. And knowing they are AI generated may not matter: In Loftus’s MIT study, labeling content as “AI-enhanced” didn’t prevent false memory formation. We would probably tend to defer to AI-generated videos simply because they resemble the kind of external record we’re used to treating as incontrovertible truth. Sora capitalized on every one of these dynamics: synthetic video of yourself, in your pocket and infinitely rewatchable, stealthily inheriting the authority already granted to the phone’s camera roll.
To illustrate how these forces compound, Loftus offered her own poignant memory. “My house burned down in a large fire in Los Angeles. This is when I was in high school. But it happened to have appeared in a magazine—there were photographs.” She’d consulted that magazine repeatedly over the years, the way Deutsch might end up returning to his Mount Rushmore video. “And my entire memories are just what’s in this magazine. Now, if you asked me anything else that isn’t a picture here, I think I’d have trouble telling you.”
The external record of an event, repeatedly visited, becomes what you remember. This strikes me as why labels and tech literacy can only go so far in protecting our minds from what Loftus’s MIT study calls “synthetic memories” or memories implanted by AI of events that never occurred. You can know exactly how the trick works and still fall for it because metacognition doesn’t override encoding. The kinds of proposed fixes I’ve heard of—things like labels, disclaimers, AI literacy initiatives—will probably help but only partially, because they assume that knowing is enough, that we have a level of conscious awareness of, and control over, memory formation that, biology suggests, we simply don’t have.
Of course, there’s one big difference between Loftus’s memory of the house fire and Deutsch’s fanciful scaling of Lincoln’s nose. One was real, the other wasn’t. Not only unreal, but unlikely. “I would make a distinction between something that’s plausible and implausible,” she said. “If suddenly there’s a picture of you in a Russian prison in Siberia and you’ve never been, you’re obviously going to be able to reject it. Maybe you’ll have a weird feeling seeing yourself, but you’re just going to know you’ve never been.”
Fair enough. I started to think that maybe it’s a stretch to say that Deutsch’s “twitch of confusion” is anything to be concerned about. But then I talked to another Sora user and things got weirder.
Elena Piech is an interactive producer who has spent years building immersive experiences like virtual reality for major entertainment and technology companies. She had been experimenting with AI video tools for months, but something about Sora was different. When we talked, she was trying to pin down what exactly was happening when she watched herself in imaginary scenes. She gave an example: a video of her avatar watching a huge screen overlooking a Blade Runner-style futuristic city.
She said she could describe the panorama of that scene, what it felt like to be there, overlooking the city, even though the place doesn’t actually exist, and that she knows she couldn’t have visited.
What Piech was describing, I realized, involved spatial memory: the brain’s capacity to encode and reconstruct the three-dimensional layout of environments you’ve inhabited. When you remember your childhood bedroom, you don’t just recall an image; you can mentally rotate through the space, sense where the door was relative to the window, feel the room’s proportions. The hippocampus is central to this process, building what neuroscientists sometimes call cognitive maps—internal models of space constructed from actual navigation and sensory experience. Normally, fiction doesn’t produce this kind of encoding. Piech told me she’d recently started watching Friends for the first time and found she couldn’t do the same thing. The set stayed two-dimensional, flat, a place observed but never inhabited. But with her Sora generations, Piech said she could feel the spatial layout around her synthetic self, a sense of the panorama and depth of a Blade Runner cityscape that couldn’t possibly exist. She described it as a “3D mind map,” a visceral sense of the space she usually associates with places she’s actually been. If Deutsch had described a neural ripple, Piech was describing a wave—an electrochemical disturbance that didn’t dissipate but propagated until it lapped the shores of distant brain regions, setting off the encoding of spatial memories for places she’d never visited.
David Pillemer, an emeritus professor of psychology at University of New Hampshire who studies how specific moments lodge in memory and shape our lives, offered a clue. When a memory includes a visual image, he told me, the person remembering it is more likely to believe it actually happened. Seeing yourself in the scene is a hallmark of vivid memories.There’s an evolutionary logic to this, he explained. “If your life was in danger 5,000 years ago and you were at the water hole and the tiger came up, if you have a visual image of what happened, it’s good to not only hold that image, but believe the image, trust it. You’ll avoid that water hole.” The visual doesn’t just record experience; it confers credibility. I thought about the yoga teacher—the French acrobat with dreads, the studio, the spot where my wife says we sat. Her evidence was a lifelike mental image. Mine was an argument. Pillemer had just told me which one the brain trusts. And that ancient trust, calibrated over thousands of generations to actual waterholes and actual predators, doesn’t have a mechanism to determine whether the image was rendered on a server farm.
Piech’s experience suggests that Sora videos could activate spatial memory, meaning that Sora videos also tripped up the brain’s more fundamental systems for sorting real from imagined. “Although it may be disconcerting to contemplate,” as cognitive psychologist Marcia K. Johnson wrote in a 2006 paper, “true and false memories arise in the same way. Memories are attributions that we make about our mental experiences based on their subjective qualities, our prior knowledge and beliefs, our motives and goals, and the social context.” Johnson’s work on source monitoring, which is the brain’s process for sorting reality from imagination, revealed there’s no tag, no stamp in the brain that says this actually happened. Instead, a scene’s qualities during recall—how vivid it is, how spatially coherent, whether it arrives unbidden or requires effort to reconstruct—are what make it feel real or imagined. Memories of actual events are usually richer, more embedded in space and context. Imagined scenes, or recollections of scenes from movies, tend to feel thinner, more schematic. But the distributions overlap, and the brain relies on these imperfect cues to sort memory from imagination.
The trouble is that these cues can mislead. If remembering a synthetic experience activates the brain just widely enough—rich perceptual detail, spatial depth, the feeling of having been somewhere, of having been with someone—it stops registering as fantasy and starts registering as memory. Piech’s recollection of Sora generations were arriving with enough of those qualities to blur the distinction.
I expected that the fantastical or outlandish videos would have unsettled Piech the most, but that wasn’t the case. Most unsettling were the videos she asked to be set in her apartment, which Sora had apparently extrapolated from the background of her initialization video: a glimpse of TV, two picture frames on the wall, enough for the model to generate something that felt, as she put it, “65 percent there.” The wall colors roughly right, the TV in the right place, the pictures close enough. Her first instinct was that OpenAI had somehow accessed her camera roll. They hadn’t; Sora had just guessed well enough—presumably from the selfie snippets she used to initialize the app—to briefly fool her about her own living room.
This is the plausibility threshold Loftus was pointing out. A Russian prison is easy to reject—there are other, more systematic cognitive processes that check what feels familiar against what you know, and you know you’ve never been to Russia. But your own apartment at 65% fidelity sits in a zone of ambiguity. It activates familiarity circuits, which run through the perirhinal cortex and operate partly beneath conscious awareness. When Piech’s Sora-generated apartment matched enough features of her actual living room—TV placement, wall color, the pictures close enough—it activated perirhinal neurons, recruiting enough neural corroboration to slip past whatever rational defenses would reject it as synthetic. It started to feel real.
Then there’s what happened with the jet-ski video. Piech and Deutsch know each other, and Sora let users grant permission to appear in each other’s generations, so Deutsch made a video of the two of them jet-skiing on the East River in a gang called the Barracudas, talking smack to tourists on the ferry. Piech laughed when she watched it, but she also had an odd sensation. “It’s weird describing this to you,” she said. “Obviously it’s just a video. But it kind of does feel like—oh yeah, we hung out. Somehow my brain’s like, yep, that’s a social interaction.” The neural wave had propagated further, reaching brain areas that encode not just place but social connection.
I don’t know what to make of all this. A faint spatial memory of a place that doesn’t exist, a glimmer of social connection from an interaction that never happened—these might seem like neurological curios, oddities to file away in an academic’s desk but nothing to spill 5,000 words over. But I find myself unsettled in a way I can’t quite shake. Sora wasn’t just producing AI slop. The neural systems being activated here—the ones that register social connection, that lay down the raw material of who you are, that sort real from imagined—aren’t supposed to be accessed by a random app. They’re supposed to require actual experience. And yet.
I’m not going to tell you how worried you should be. But I want to think through what this might mean.
But I find myself unsettled in a way I can’t quite shake.
One potential consequence is how these tools could shape identity, at scale. I was particularly taken by a term Deutsch coined: propagandi, or propaganda directed at yourself. If propaganda works by shaping collective memory, propagandi is more atomized, more intimate. You’re the propagandist and the mark, constructing a version of yourself that doesn’t exist, for an audience of one. I called Northwestern University psychologist Dan McAdams to help me stress-test Deutsch’s speculation. McAdams developed the influential concept of narrative identity—the idea that identity is built from autobiographical memories, that the self you’ll be tomorrow is constructed from the memories you have today. Contaminate the memories, and the identity may shift. When I described what Sora users like Deutsch were experiencing, McAdams said he hadn’t heard of the phenomenon yet, “but a moment’s reflection suggests that it is inevitable.” These AI videos could “ultimately be encoded and reworked as ‘things that happened to me,’ and then perhaps ‘important things that happened to me that are now part of my life story.’” Propagandi, in other words, isn’t just a clever coinage. It names a mechanism for rewriting who you are.
A hopeful read isn’t hard to find. Piech made a K-pop dance video of herself, fluid and confident, moving in ways she can’t. After watching it a few times, she told me, she started to feel like maybe she actually could. Athletes have used visualization for decades; maybe Sora was just a more vivid format. Therapists working with trauma have long known that memory can be beneficially malleable; perhaps tools like Sora, carefully deployed, could help people revise the scenes that haunt them.
But consider who’s building these tools. OpenAI confirmed that user prompts and outputs trained the model by default; meanwhile, videos which were saved, shared, or regenerated almost certainly shaped the feed. Users save confident-looking videos and regenerate awkward ones. Across millions of interactions, the system drifts towards flattery. More than a decade of social media research has documented the harm of exposure to idealized images of others. But there’s always been an escape hatch: The comparison is to someone else. The escape hatch works because comparison requires holding self and other apart. What happens when the idealized image is you? What the memory research suggests is that Sora generations could have, with time, slipped beneath that defense. The gap between your real autobiography and your synthetically infused, commercially tainted one drives a pervasive sense of inadequacy, as your actual life fails to live up to a narrative identity that was never yours to begin with. “What people could do with marketing and this technology is making a lot of people salivate right now,” Deutsch wearily noted.
Imagine how this plays out for a 17-year-old girl who’s been on the app for months. She’s given it her face, her voice, her mannerisms. The app knows from her browsing that she’s been looking at prom dresses. But the video that appears in her Sora feed isn’t anything special—it’s just her, in her own bedroom, getting ready for school on what looks like a normal morning, wearing a dress from a brand she can’t quite afford. The synthetic version of her isn’t doing anything extraordinary. She just looks like herself on a slightly better day. Skin a little clearer, hair a little more together. The dress, by the way, is tagged and purchasable. On Instagram, in viewing someone else’s photos, she’d be comparing herself to someone else, and there are more psychological defenses against that: The other person’s feed is curated, it’s not real life, and so the comparison isn’t fair. This defense doesn’t always work, but it’s there. Sora was different. The feeling isn’t envy. It’s closer to confusion. Not I wish I looked like her but Why don’t I look like that? or even more insidious, Why don’t I look like myself? And if she’s been watching these videos for months, memory research suggests that remembering the AI videos won’t register as being memories of AI videos. They’ll feel vaguely like mornings she half-remembers, days when things just came together a little more easily. Each actual morning, in her actual mirror, will begin to always feel like an off day, which is precisely the feeling the whole experience was engineered to produce, and precisely the feeling a “Buy” button is eagerly positioned to resolve.
But something else was nagging at me, in addition to the potential psychological consequences: Even something as intimate as autobiographical memory doesn’t form in isolation. It’s fundamentally social. In a process scientists endearingly call maternal reminiscing, children learn to shape experience into story through dialogue with caregivers, a process that continues throughout life: the friend who leans in or looks skeptical, the partner who remembers it differently, the listener who asks a question that reframes the whole event. Even the distraction level of the listener can affect how well we remember our own memories. In one experiment, a psychologist had participants tell a story to a friend who was secretly distracted. A month later, the speakers remembered their own experience less well simply because of how a listener behaved during their retelling of it. The attentive listener isn’t just receiving the memory; they’re helping to construct it.
Now imagine referencing something your friend doesn’t share, because it never happened. The blank look. The awkward silence. You might question yourself, wondering if you imagined it. You might question them. Or you might learn to stop bringing it up altogether, retreating from actual human social interaction to more AI simulacra of human social interactions, which never push back, which always affirm. The false memory, born in isolation, produces isolation again when it enters conversation.
Piech seemed to be thinking about this, even if she wasn’t citing social psychology to back her intuitions up. Her partner was traveling for two months, and Piech found herself pondering whether she could use Sora to maintain a sense of connection—generate videos of them together, something to watch when she missed her partner. Then she thought through what that would actually mean: one person accumulating an archive of shared experiences the other had never seen, building memories of a relationship that only existed on one side. “What if I watched all of them and she didn’t watch them,” she said, “and now I’m referring to these things that she has no idea about?” She decided not to make the videos.
Piech stopped because she wondered what Sora might do to her relationship. I couldn’t even get started. I’d pulled up Sora’s App Store page at least twice, downloaded the app, then deleted it. I’d then toggle the invite text message back to “unread” and swear to think about it harder, later. I already had reasons not to download Sora—the usual ones, about data privacy and the general question of whether the world needs more AI-generated slop. Those are the reasons I’d give if you asked me at a dinner party. They’re rational, articulable, and they were all in place before Deutsch told me about his bizarre memory twitch.
What I likely underestimate is my own vulnerability to this technology. The psychological and neurological mechanisms that sort real from imagined—the ones Pillemer described, the ones Johnson mapped, the ones Piech felt activate while she watched herself in a city that doesn’t exist—don’t always check in with the part of you that might know better. They don’t consult your AI literacy or your PhD or your healthy skepticism about OpenAI’s privacy policy. They run underneath all of that, and they trust pictures.
The yoga class thing is amusing, but it truly is a simple question of whether my wife has a false memory, or I have forgotten something that really happened. I honestly have no idea which it is, and as confident as I am that I’m right, neuroscience suggests this confidence is unwarranted, and I accept that. There was a period, years later, when my wife and I had differing accounts of a consequential series of events. For a while, we couldn’t talk about the subject at all. Not a misremembered yoga instructor but a stretch of our life together that we had apparently lived through twice, once each, in parallel versions that couldn’t be reconciled. I won’t get into the details. This was somewhat different from the yoga instructor. Here, two people agreed they were present for the same events in real time, but experienced them differently (for all the reasons this happens—hunger, emotional state, past experiences, attention, etc). That difference in experience, in turn, led to different memories—the way we suppress certain details, cast an action in a different light. You could say one of us was remembering right and one of us was remembering wrong, but the reality is more complex. We were remembering things that happened the way memories are always made: subjectively and idiosyncratically. Memory, with time, is essentially storytelling. And with time, we settled into two separate narratives each replete with its own accompanying details.
The hardest part wasn’t the disagreement itself, but what it took away: the quotidian ability to say Remember when? and have the other person nod. You don’t realize how much of a relationship runs on the shared archive, the stuff you can both reach for without negotiation until it’s not there. We got through it, but what we arrived at wasn’t closure, the way a judge would rule on the facts of the case. It was something richer and truer to the human experience: that another person’s memory and experience can diverge from yours, and that to love someone is to accept rather than be threatened by this divergence—that a perfectly duplicate shared memory bank is not a prerequisite for building a life together.
My wife and I made our divergent narratives the old-fashioned way, with proximity and time and two brains encoding and decoding the same events differently. Sora did it in a closed loop between you and a screen. No one to push back, no one to say that isn’t what happened. By the time the memory enters a conversation with someone who actually shares your life, it has already hardened into something that feels like yours.
Some nights after our son is asleep, my wife and I sit on the couch and reconstruct the day for each other. What he said at breakfast, the weird thing he did with his yogurt spoon, whether the stalling tactics at bedtime were really that outlandish or whether we were both just tired. Sometimes we seem to disagree on the details, even if we were both there. She’ll point out something I didn’t notice, or I’ll interpret something we both noticed differently, or she’ll add a layer of interpretation by connecting his actions to similar actions the day before. The narrative shifts a little, adjusts a little to accommodate both of us, and by the time we’ve moved on we both begin to consolidate memories of something neither of us quite experienced—which, in the end, is the uncomfortable truth: Memory and experience are not synonymous. I used to think of this process as more akin to fact-checking, of sifting fact from embellishment, reality from interpretation. But it’s not quite that. It’s something more meaningful than checking facts: sitting there, remembering them together.
Tim Requarth is director of graduate science writing and research assistant professor of neuroscience at the NYU Grossman School of Medicine, where he studies how artificial intelligence is changing the way scientists think, learn, and write. He writes “The Third Hemisphere,” a newsletter that explores AI’s effects on cognition from a neuroscientist’s perspective. His essays and reporting have appeared in The New York Times, The Atlantic, and Slate, where he is a contributing writer.
Editor:Krista Stevens Fact-checker: Julie Schwietert Collazo Copyeditor: Cheri Lucas Rowlands
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