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Nav Toor

@heynavtoor

Jun 19

Researchers analyzed 14 million academic papers published between 2010 and 2024. They tracked every word. They found that ChatGPT is rewriting the English language.

Not metaphorically. Literally.

After ChatGPT launched in November 2022, certain words that had been stable in academic writing for over a decade suddenly exploded in frequency. The researchers at the University of Tübingen and Northwestern University mapped every excess word and categorized them.

The words are ones you already recognize.

"Delve." "Intricate." "Meticulous." "Commendable." "Underscore." "Pivotal." "Nuanced." "Landscape." "Comprehensive." "Multifaceted." "Showcasing." "Groundbreaking." "Innovative." "Invaluable."

329 excess style words appeared in early 2024 that were not there before. The spike is unprecedented in the history of the dataset.

Here is what makes this different from every other vocabulary shift ever recorded. During COVID, excess words also appeared. Up to 188 of them in 2021. But those were content words. "Respiratory." "Remdesivir." "Ventilator." Words that described a new reality.

After ChatGPT, the excess words are not content words. They are style words. Not what people write about. How people write. The subject matter did not change. The voice did.

The researchers estimate that at least 10% of all academic papers published in 2024 were processed with ChatGPT. Not written entirely by AI. Processed. Edited. Polished. Run through the model and published with its fingerprints still on the page.

You have seen these words everywhere. In emails. In LinkedIn posts. In articles. In cover letters. In reports your colleagues sent you. You could not explain why everything started sounding the same. Now you can. The entire internet passed through the same model. And the model left the same fingerprints on everything it touched.

The researchers proved something else. The contamination is not slowing down. The number of excess words grew from 188 during COVID to 329 after ChatGPT. The curve is still climbing.

ChatGPT did not just change what we can do with language. It changed the language itself. One model. One voice. Fourteen million papers. And a vocabulary shift larger than a global pandemic.

1/ The word "delve" tells the whole story.

Before ChatGPT, "delve" appeared in roughly 1 in 1,000 PubMed abstracts. Stable for a decade. Then ChatGPT launched. The frequency shot up vertically. In 2024 it appeared in roughly 3 in 1,000 abstracts. A tripling in one year.

One word. One model. Fourteen million papers.

2/ The full list of ChatGPT's fingerprints.

These are the words that exploded after ChatGPT launched:

Delve. Intricate. Meticulous. Commendable. Underscore. Pivotal. Nuanced. Landscape. Comprehensive. Multifaceted. Showcasing. Groundbreaking. Innovative. Invaluable. Leverage. Harness. Foster. Embark. Garner. Exemplary.

329 excess style words in total. All appearing at rates that never existed before November 2022.

Read 7 tweets

Nav Toor

@heynavtoor

Jun 16

The Dead Internet Theory was a conspiracy. The idea that the internet is no longer human. That bots and AI have quietly replaced real people. It started on anonymous message boards in 2019. Most people dismissed it.

Stanford, Imperial College London, and the Internet Archive just measured it.

They used the Wayback Machine to scan every new website published between 2022 and 2025. Thirty-three months of the internet, captured and classified. They applied one of the most advanced AI text detectors in the world to every page.

35.3% of all newly published websites were AI-generated or AI-assisted.

17.6% were completely AI-generated. No human involvement at all.

In late 2022, before ChatGPT launched, that number was zero.

In three years, more than a third of the new internet became synthetic. Not over decades. Not over a generation. Three years.

Then they measured what that is doing to the internet itself.

Semantic diversity is falling. The range of ideas, perspectives, and ways of saying things is narrowing. As AI content increases, the internet sounds more and more like one voice. Because it is one voice. The same models producing the same patterns across millions of pages.

Positive sentiment is rising. Everything sounds upbeat. Polished. Confident. Helpful. The internet is getting friendlier while getting emptier. The tone improves as the substance disappears.

The lead researcher, Jonáš Doležal at Imperial College London, said this to 404 Media: "I find the sheer speed of the AI takeover of the web quite staggering. After decades of humans shaping it, a significant portion of the internet has become defined by AI in just three years."

Separately, Cloudflare reported that nearly a third of all internet traffic now comes from bots. Imperva reported that automated traffic surpassed human traffic for the first time in 2024.

If you read my previous threads on Model Collapse and Retrieval Collapse, this is the final chapter. Model Collapse showed that AI trained on AI gets dumber. Retrieval Collapse showed that search engines indexing AI content get emptier. This paper shows the source of both problems. The internet itself is being replaced.

The researchers are now working with the Internet Archive to build a live monitoring tool. A real-time tracker of how much of the internet is human and how much is not.

The fact that we need a tool to measure how much of the internet is still real is the finding.

1/ The growth curve.

In late 2022, the share of AI-generated websites was zero.

By mid-2023, it was 10%.

By mid-2024, it was 20%.

By mid-2025, it was 35.3%.

The red line is fully AI-generated. The purple line includes AI-assisted. Both are climbing. Neither has slowed down.

2/ The internet is getting more similar.

They measured semantic diversity. How different websites are from each other in meaning and ideas.

As AI content increases, diversity falls. The correlation is statistically significant (ρ = 0.47, p = 0.004).

AI-generated pages are 33% more semantically similar to each other than human-written pages. The internet is converging toward one voice. Because it is one voice.

Read 7 tweets

Nav Toor

@heynavtoor

Jun 15

A Stanford neuroscientist said something on his podcast that most adults do not want to hear.

Heavy phone use can cause adult ADHD in people who never had it.

The fix takes 30 days. It costs nothing. Almost no one will try it.

1/ The dopamine reset most adults need.

Most adults who think they have ADHD do not have ADHD.

They have something else.

Andrew Huberman said it plainly. Heavy phone use floods the brain with too much input. Email. Texts. Three apps. Two real talks. Fifteen tabs. All at once.

Your brain stops being able to focus on one thing. You trained it to expect a new hit every six seconds.

Huberman calls it a form of ADHD. He said the brain can start to look just like a brain with real ADHD. The good news is that it can heal.

2/ A 2020 brain scan study proved this is not just a theory.

Scientists used a PET scan to study 22 healthy adults. None of them had ADHD.

They tracked each person's daily phone use for weeks.

The result was clear. The more time someone spent on social apps, the lower their dopamine levels were in a key part of the brain called the putamen.

The putamen is the same part of the brain that is broken in real ADHD.

Heavy phone use does not just feel like ADHD. It makes the brain look like ADHD on a scan.

Read 9 tweets

Nav Toor

@heynavtoor

Jun 13

You have noticed that too. Google Search is getting worse. The results look professional but say nothing. The answers are longer but less useful. Every page reads like it was written by the same voice.

You thought Google was broken. It is not broken. It is being replaced.

Researchers published a paper at the ACM Web Conference 2026 proving what is happening. They call it Retrieval Collapse.

Here is the mechanism in one sentence. AI-generated content is flooding the internet so fast that search engines are now showing you mostly AI-written pages. And the search engine cannot tell the difference.

They ran a controlled experiment. They started with a pool of real, human-written web pages. Then they gradually added AI-generated content until it made up 67% of the pool.

By that point, over 80% of the top search results were AI-generated. Not 67%. Over 80%. The ranking algorithm did not just let AI content in. It preferred it. The AI-written pages were better optimized, more fluent, and more keyword-rich than the human pages. They outranked the originals.

Here is the part that makes this invisible.

Answer accuracy stayed the same. The search results still looked correct. The information was still technically right. If you measured quality by accuracy alone, nothing appeared wrong.

But source diversity collapsed. Nearly every result came from the same type of content. AI-written. AI-optimized. AI-structured. The human-written pages, the ones with original reporting, personal experience, and genuine expertise, were buried.

The researchers describe a two-stage collapse. Stage one is Dominance. High-quality AI content silently takes over the top results. Everything looks fine. Accuracy is stable. Nobody notices. Stage two is Corruption. Once AI dominates the pipeline, adversarial and low-quality content starts slipping through. By then, the system is too dependent on synthetic sources to course-correct.

A separate analysis found that 74.2% of newly published web pages now contain AI-generated content. Organic click-through rates on pages with AI summaries have dropped 61%. The human internet is being outranked by the machine internet.

Model Collapse described what happens when AI trains on AI. The models get dumber. Retrieval Collapse describes what happens when search engines index AI. The results get emptier.

Both are happening right now. At the same time. And neither one looks broken from the outside.

The search engine still returns ten blue links. The links still load. The pages still answer your question. But the thing that used to make those answers trustworthy, a human who actually knew something, is being quietly replaced by a machine that sounds like it does.

1/ The amplification effect in one chart.

The researchers started with 0% AI content. They added more each round.

At 33% AI in the pool, 43% of your search results were AI.
At 50% AI in the pool, 68% of your results were AI.
At 67% AI in the pool, 81% of your results were AI.

The algorithm does not reflect the ratio. It amplifies it. AI content outranks human content at every level.

2/ The deception in the numbers.

Round 0: 0% AI content. Answer accuracy 68.17%.
Round 20: 81% AI content. Answer accuracy 67.68%.

Read those two lines again. The search results went from fully human to 81% synthetic. The accuracy barely moved.

That is what makes this invisible. The grades did not change. The source of every answer did. Nobody checking accuracy would notice. The collapse is hidden behind a stable score.

Read 8 tweets

Nav Toor

@heynavtoor

Jun 13

I was in a Starbucks in Brooklyn when my MacBook died at 2:47 PM.

I had charged it that morning to 100%.

I assumed the battery was old. Or broken. I almost paid Apple $249 to replace it.

Then a friend who works at Apple showed me 10 settings macOS turns on by default. I changed all of them.

The next day, the same MacBook lasted till 11 PM on one charge.

Here's the full list.

Setting 1: Turn on Low Power Mode.

This single setting adds 1.8 to 2.4 hours of battery life on M-series MacBooks.

How to:
System Settings → Battery → Low Power Mode → "Only on Battery"

It pauses heavy syncing, drops CPU when idle, and reduces background tasks. Apple's own feature. Free. Underused.

Setting 2: Stop Wake for Network Access.

Your MacBook wakes up every few minutes while sleeping to check email, sync iCloud, and ping the network.

How to:
System Settings → Battery → Options → Wake for Network Access → Never (or Only on Power Adapter)

This alone fixes the "my MacBook lost 30% overnight" problem.

Read 15 tweets

Nav Toor

@heynavtoor

Jun 13

In 1944, a 13-year-old Jewish boy watched the Nazis take Hungary.

His father gave the family fake Christian names. Forged papers. Split them apart so if one was caught, the others might live.

The boy hid as the godson of a government official. 500,000 Hungarian Jews were killed in 8 months. He survived.

He arrived in London with nothing. Worked as a railway porter. Slept in train stations.

48 years later, he placed a $10 billion trade against the British pound.

By nightfall, he had made $1 billion in a single day. The press called him "The Man Who Broke the Bank of England."

His name was George Soros. His book "The Alchemy of Finance" has stayed in print since 1987.

I turned his philosophy into 12 prompts.

Here are all 12:

1. Reflexivity Detection

Soros built his fortune on one idea most economists reject. In The Alchemy of Finance he wrote: "I contend that financial markets never reflect the underlying reality accurately; they always distort it in some way or another, and the distortions find expression in market prices." Reflexivity is the feedback loop where beliefs shape prices, prices shape reality, and that reality shapes beliefs again. Spot the loop early and you see the bubble before the crowd do

PROMPT

"I'm trying to understand a market, trend, or situation where belief and reality seem to be feeding each other. Here is my situation: [describe]. Using George Soros's Reflexivity Detection framework, analyze my position:

1. Where is the feedback loop here? Soros said market prices distort reality rather than reflect it. How are participants' beliefs actively changing the thing they are betting on?
2. What belief is currently driving prices or behavior, and how is that belief altering the underlying fundamentals in return?
3. Is this loop self-reinforcing right now, building the trend higher, or has it started to reverse?
4. What evidence would tell me the gap between perception and reality has stretched too far to hold?
5. Give me one specific action this week to position for the moment the loop breaks instead of getting trapped inside it."

2. The Boom-Bust Anatomy

Soros saw every bubble as a sequence, not an accident. A trend becomes self-reinforcing as belief and price push each other higher, until the distance from reality grows too wide and it collapses. He warned: "Markets are constantly in a state of uncertainty and flux." Knowing which stage you stand in changes everything. Early in the boom you ride it. Late in the boom you prepare to run.

PROMPT

"I'm looking at a trend, asset, or market and I need to know which stage of the boom-bust cycle I am in. Here is my situation: [describe]. Using George Soros's Boom-Bust Anatomy framework, analyze my position:

1. Map this trend onto the boom-bust cycle. Is it early, accelerating, near the peak, or already turning? Soros said trends become self-reinforcing until they collapse.
2. What is the prevailing belief powering this trend, and how far has price moved beyond the underlying reality?
3. What are the signs the self-reinforcing phase is exhausting itself? Where is the fuel running low?
4. If this is late in the boom, what is my exit plan, and what is my trigger to act on it?
5. Give me one specific action this week that matches the stage I am actually in, not the stage I wish I were in."

Read 15 tweets

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