
# Nvidia's November 2025 "Flash Crash": Did Wall Street Rig NVDA – Or Was It Just a Crowded Trade Unwinding?

---

## 1. What actually happened: Nov 19-21, 2025 in three days

In the three trading days from Wednesday 19 November through Friday 21 November 2025, Nvidia went from a textbook post-earnings celebration to what felt, on screens, like a near-crash. Record results and raised guidance sent NVDA jumping at first, but by Friday’s close the stock had swung almost 12% from peak to trough — enough to make traders wonder if they had just watched a clean-up of crowded AI positioning or something darker. ([NVIDIA Newsroom][1])

Here’s the three-day picture (US Eastern Time, regular sessions only):

| Date (2025)  | Open   | High   | Low    | Close      | Day move | Notes                          |
| ------------ | ------ | ------ | ------ | ---------- | -------- | ------------------------------ |
| Nov 19 (Wed) | 184.79 | 187.86 | 182.83 | **186.52** | +2.9%    | Earnings after the close       |
| Nov 20 (Thu) | 195.95 | 196.00 | 179.85 | **180.64** | -3.1%    | Huge gap up, then big reversal |
| Nov 21 (Fri) | 181.24 | 184.56 | 172.93 | **178.88** | -1.0%    | Volatile, closes slightly red  |

Key points:

* On **Nov 19**, NVDA closed at **$186.52**, up ~2.9% after trading between roughly **$182.8 and $187.9**.
* On **Nov 20**, it **opened $195.95**, **ticked $196.00**, then slid to **$179.85** before closing **$180.64** (about **-3.1%** vs Wednesday’s close but more than **8%** off the intraday high).
* On **Nov 21**, it opened at **$181.24**, ranged **$184.56 to $172.93**, and finished **$178.88** (about **-1%** on the day and **~11.8%** below Thursday’s high).
* From Thursday’s **$196.00** high to Friday’s **$172.93** low is almost a **12%** swing.
* By **Monday 24 November**, NVDA was back in the low **$180s**, still well below the late-October record close around **$207**. ([MacroTrends][3])

### Snapshot: NVDA on Monday, 24 November (intraday)

* Latest price **$182.55**, change **$4.00 (0.02%)** vs previous close.
* Open **$179.42**; high **$183.41**; low **$176.60**; volume **253,434,849**.
* Latest trade time **15:11:44 PST**.

The tape looks wild, but the news flow was overwhelmingly positive. That gap between “great fundamentals” and “bad price action” is what fueled the “rigged” and “front-running the jobs report” chatter.

---

## 2. Earnings were exceptional, not the problem

After the close on **Wednesday, 19 November**, Nvidia reported:

* **$57.0B** in quarterly revenue for Q3 FY26 (ended 26 October 2025), up **22% QoQ** and **62% YoY**.
* **$51.2B** in data-center revenue, up **66% YoY**.
* Guidance for roughly **$65B** next quarter.
* Management reiterated AI accelerator demand was **“off the charts.”** ([NVIDIA Newsroom][1])

Coverage from Reuters, TechCrunch, the Guardian, and others framed it as another emphatic beat that briefly eased AI-bubble jitters and sparked a global relief rally. The earnings release **added** to bullish sentiment; it didn’t cap it. ([The Guardian][7])

---

## 3. The macro backdrop: a delayed jobs report hits with earnings

All times below are **US Eastern Time (ET)**.

### Thursday, 20 November 2025

Because a 43-day federal shutdown delayed official statistics, the **September Employment Situation** report landed on **Thursday 20 November at 08:30 a.m. ET**, alongside weekly jobless claims — an unusual overlap with Nvidia’s earnings glow. ([Bureau of Labor Statistics][5])

Headline numbers:

* Payrolls: **+119k** vs expectations closer to **+50k**.
* Unemployment rate: **4.4%**, the highest in several years.
* Prior months **revised down**.

### Friday, 21 November 2025

* **08:30 a.m. ET** - BLS releases **September Real Earnings**.

### Crucial timing

* **All the macro prints hit at 08:30 a.m. ET.**
* The **US cash equity session opens at 09:30 a.m. ET**.
* On both days, the macro data were public for **an hour** before regular-session trading in NVDA began.

---

## 4. The sequence: earnings pop, jobs data, then a positioning unwind

### Wednesday evening into Thursday pre-market

* After hours on **Nov 19**, NVDA jumped about **5%**, adding well over **$200B** in market value within minutes. ([Reuters][4])
* Between **8–9 a.m. ET** on **Nov 20**, pre-market updates repeatedly showed NVDA **up 5%+**, dragging Nasdaq futures higher (Nasdaq futures **>+1.5%**). ([Reuters][4])
* **08:30 a.m. ET** — Jobs report and claims drop. Markets digest a “no quick rate cuts, but no collapse either” narrative. Nvidia remains elevated into the open; there is **no evidence of a dump before 8:30**. ([Bureau of Labor Statistics][5])

---

### Thursday cash session (20 Nov)

* **09:30 a.m. ET** — NVDA opens **$195.95**, ticks **$196.00** near the open. Indices gap up hard: Dow +700, Nasdaq +2.5% within minutes. ([Investopedia][6])
* **Late morning (≈10:30–10:45 a.m.)** — Nvidia and the indices **roll over**. The S&P 500 and Nasdaq slide from early gains of ~2% to losses over 1.5%, a rare intraday reversal more typical of stress days.
* **Low:** NVDA hits **$179.85**, an **~8.2%** drop from the **$196** high.
* **Close:** **$180.64**, down **~3.1%** on the day and **~7.8%** below the pre-market high. ([StockAnalysis][2])

Market color during the slide focused on profit-taking in a crowded AI leader, options-driven hedging (dealers short gamma selling into weakness), and a reassessment of how much to pay for a stock that had already doubled on the AI story. There were **no major new headlines** driving the 10:30–4:00 tape.

---

### Friday cash session (21 Nov)

* **Open:** **$181.24**, a touch above Thursday’s close.
* **Range:** **$184.56** high to **$172.93** low.
* **Close:** **$178.88**, about **-1%** on the day and **~11.8%** below Thursday’s high. ([StockAnalysis][2])

The broader market stabilized and even finished green, helped late by Fed commentary. Nvidia stayed weak, pointing to stock-specific positioning rather than fresh macro shock.

---

## 5. Why people think “it started before the jobs report”

Two mundane issues create the illusion of pre-data selling:

1) **Time zones.** Economic calendars quote **08:30 a.m. ET**; many charts default to **UTC**. **08:30 ET = 13:30 UTC.** If you compare a UTC-stamped calendar to an ET-stamped chart, it can look like the stock moved first when you are staring at the same moment in two clocks. ([Bureau of Labor Statistics][5])

2) **Session coverage.** Many retail charts show only the **9:30–16:00 ET** cash session. The first reactions to both the earnings release (after hours) and the jobs data (pre-market) happen **before** those charts even draw a candle. By 9:30, the info is already reflected; what you see next is the positioning unwind.

3) **Instrument mix.** Futures and FX trade almost 24/5; single-stock pre-market liquidity is thinner and cash opens later. Overlaying futures with cash equities without adjusting time windows can create false leads/lags.

Once you align clocks and sessions, the sequence looks straightforward: **earnings pop → jobs data digested → late-morning unwind in a crowded trade.**

---

## 6. What actually drove the reversal

Beyond the calendar mechanics, the drivers were classic:

* **Crowded positioning.** Nvidia was the flagship AI trade, the world’s most valuable listed company, and a core holding in AI-focused ETFs. Even “great” numbers can trigger “good enough to take profits.”
* **Options and systematic flows.** Elevated implied volatility around earnings + macro means dealers hedging short gamma can sell stock as it drops, reinforcing the move. Stop-loss orders and CTA/vol-targeting rules can add momentum.
* **Valuation check.** After doubling (and then some) on the AI story, investors reassessed how much they would pay in a session that started euphoric and flipped to risk-off.
* **No fresh negative headline.** Media coverage at the time explicitly noted the reversal came with “no major headlines” — it was flow- and positioning-driven. ([Investopedia][6])

---

## 7. Is there evidence of manipulation?

To move from “ugly tape” to “illegal manipulation,” you’d expect specifics: non-public access to the jobs report before 8:30, documented spoofing/layering patterns, or regulator/exchange notices flagging abnormal NVDA trading. ([Bureau of Labor Statistics][5])

Based on public information, none of those exist for **19–21 November 2025**, despite intense media attention. Major outlets explained the move with standard factors: positioning, valuations, AI-bubble concerns, and options hedging. ([Reuters][4])

**Conclusion:** The price path is consistent with a crowded trade being unwound into an earnings + macro window, not a smoking gun of collusion.

---

## 8. Practical takeaways

* **Don’t anchor on the close.** “Nvidia down 3%” misses an **8% intraday swing** and an **~12% peak-to-trough** over two sessions.
* **Great fundamentals can still be “sell the news.”** When expectations and ownership are stretched, excellent prints can be liquidity events for de-risking.
* **Align clocks and sessions.** Check time zones and whether charts include pre-/post-market before inferring causality.
* **Expect big moves around stacked catalysts.** Earnings + delayed macro + crowded positioning + elevated vol is a recipe for two-sided 5–10% swings, even in mega-caps.
* **Assume flow before conspiracy.** In the absence of regulatory flags, the base case is positioning and hedging mechanics.

---

*Standard disclaimer: this article is for informational and educational purposes only and is not investment advice or a recommendation to buy or sell any security.*

[1]: https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-third-quarter-fiscal-2026?utm_source=chatgpt.com "NVIDIA Announces Financial Results for Third Quarter ..."
[2]: https://stockanalysis.com/stocks/nvda/history/?utm_source=chatgpt.com "NVIDIA (NVDA) Stock Price History 1999-2025"
[3]: https://www.macrotrends.net/stocks/charts/NVDA/nvidia/stock-price-history?utm_source=chatgpt.com "NVIDIA - 26 Year Stock Price History | NVDA"
[4]: https://www.reuters.com/world/china/global-markets-trading-day-graphic-2025-11-20/?utm_source=chatgpt.com "Trading Day: Nvidia sparkle vanishes, Wall Street slammed"
[5]: https://www.bls.gov/news.release/empsit.nr0.htm?utm_source=chatgpt.com "Employment Situation Summary - 2025 M09 Results"
[6]: https://www.investopedia.com/dow-jones-today-11202025-11853411?utm_source=chatgpt.com "Markets News, Nov. 20, 2025: Major Stock Indexes Post ..."
[7]: https://www.theguardian.com/technology/2025/nov/19/nvidia-earning-report?utm_source=chatgpt.com "'We excel at every phase of AI': Nvidia CEO quells Wall Street fears of AI bubble amid market selloff"


<h1>Nvidia's November 2025 "Flash Crash": Did Wall Street Rig NVDA – Or Was It Just a Crowded Trade Unwinding?</h1>
<hr>
<h2>1. What actually happened: Nov 19-21, 2025 in three days</h2>
In the three trading days from Wednesday 19 November through Friday 21 November 2025, Nvidia went from a textbook post-earnings celebration to what felt, on screens, like a near-crash. Record results and raised guidance sent NVDA jumping at first, but by Friday’s close the stock had swung almost 12% from peak to trough — enough to make traders wonder if they had just watched a clean-up of crowded AI positioning or something darker. (<a href="https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-third-quarter-fiscal-2026?utm_source=chatgpt.com" title="NVIDIA Announces Financial Results for Third Quarter ...">NVIDIA Newsroom</a>)
Here’s the three-day picture (US Eastern Time, regular sessions only):
<table>
<thead>
<tr>
<th>Date (2025)</th>
<th>Open</th>
<th>High</th>
<th>Low</th>
<th>Close</th>
<th>Day move</th>
<th>Notes</th>
</tr>
</thead>
<tbody>
<tr>
<td>Nov 19 (Wed)</td>
<td>184.79</td>
<td>187.86</td>
<td>182.83</td>
<td>186.52</td>
<td>+2.9%</td>
<td>Earnings after the close</td>
</tr>
<tr>
<td>Nov 20 (Thu)</td>
<td>195.95</td>
<td>196.00</td>
<td>179.85</td>
<td>180.64</td>
<td>-3.1%</td>
<td>Huge gap up, then big reversal</td>
</tr>
<tr>
<td>Nov 21 (Fri)</td>
<td>181.24</td>
<td>184.56</td>
<td>172.93</td>
<td>178.88</td>
<td>-1.0%</td>
<td>Volatile, closes slightly red</td>
</tr>
</tbody>
</table>
Key points:
<ul>
<li>On Nov 19, NVDA closed at $186.52, up ~2.9% after trading between roughly $182.8 and $187.9.</li>
<li>On Nov 20, it opened $195.95, ticked $196.00, then slid to $179.85 before closing $180.64 (about -3.1% vs Wednesday’s close but more than 8% off the intraday high).</li>
<li>On Nov 21, it opened at $181.24, ranged $184.56 to $172.93, and finished $178.88 (about -1% on the day and ~11.8% below Thursday’s high).</li>
<li>From Thursday’s $196.00 high to Friday’s $172.93 low is almost a 12% swing.</li>
<li>By Monday 24 November, NVDA was back in the low $180s, still well below the late-October record close around $207. (<a href="https://www.macrotrends.net/stocks/charts/NVDA/nvidia/stock-price-history?utm_source=chatgpt.com" title="NVIDIA - 26 Year Stock Price History | NVDA">MacroTrends</a>)</li>
</ul>
<h3>Snapshot: NVDA on Monday, 24 November (intraday)</h3>
<ul>
<li>Latest price $182.55, change $4.00 (0.02%) vs previous close.</li>
<li>Open $179.42; high $183.41; low $176.60; volume 253,434,849.</li>
<li>Latest trade time 15:11:44 PST.</li>
</ul>
The tape looks wild, but the news flow was overwhelmingly positive. That gap between “great fundamentals” and “bad price action” is what fueled the “rigged” and “front-running the jobs report” chatter.
<hr>
<h2>2. Earnings were exceptional, not the problem</h2>
After the close on Wednesday, 19 November, Nvidia reported:
<ul>
<li>$57.0B in quarterly revenue for Q3 FY26 (ended 26 October 2025), up 22% QoQ and 62% YoY.</li>
<li>$51.2B in data-center revenue, up 66% YoY.</li>
<li>Guidance for roughly $65B next quarter.</li>
<li>Management reiterated AI accelerator demand was “off the charts.” (<a href="https://nvidianews.nvidia.com/news/nvidia-announces-financial-results-for-third-quarter-fiscal-2026?utm_source=chatgpt.com" title="NVIDIA Announces Financial Results for Third Quarter ...">NVIDIA Newsroom</a>)</li>
</ul>
Coverage from Reuters, TechCrunch, the Guardian, and others framed it as another emphatic beat that briefly eased AI-bubble jitters and sparked a global relief rally. The earnings release added to bullish sentiment; it didn’t cap it. (<a href="https://www.theguardian.com/technology/2025/nov/19/nvidia-earning-report?utm_source=chatgpt.com" title="&#x27;We excel at every phase of AI&#x27;: Nvidia CEO quells Wall Street fears of AI bubble amid market selloff">The Guardian</a>)
<hr>
<h2>3. The macro backdrop: a delayed jobs report hits with earnings</h2>
All times below are US Eastern Time (ET).
<h3>Thursday, 20 November 2025</h3>
Because a 43-day federal shutdown delayed official statistics, the September Employment Situation report landed on Thursday 20 November at 08:30 a.m. ET, alongside weekly jobless claims — an unusual overlap with Nvidia’s earnings glow. (<a href="https://www.bls.gov/news.release/empsit.nr0.htm?utm_source=chatgpt.com" title="Employment Situation Summary - 2025 M09 Results">Bureau of Labor Statistics</a>)
Headline numbers:
<ul>
<li>Payrolls: +119k vs expectations closer to +50k.</li>
<li>Unemployment rate: 4.4%, the highest in several years.</li>
<li>Prior months revised down.</li>
</ul>
<h3>Friday, 21 November 2025</h3>
<ul>
<li>08:30 a.m. ET - BLS releases September Real Earnings.</li>
</ul>
<h3>Crucial timing</h3>
<ul>
<li>All the macro prints hit at 08:30 a.m. ET.</li>
<li>The US cash equity session opens at 09:30 a.m. ET.</li>
<li>On both days, the macro data were public for an hour before regular-session trading in NVDA began.</li>
</ul>
<hr>
<h2>4. The sequence: earnings pop, jobs data, then a positioning unwind</h2>
<h3>Wednesday evening into Thursday pre-market</h3>
<ul>
<li>After hours on Nov 19, NVDA jumped about 5%, adding well over $200B in market value within minutes. (<a href="https://www.reuters.com/world/china/global-markets-trading-day-graphic-2025-11-20/?utm_source=chatgpt.com" title="Trading Day: Nvidia sparkle vanishes, Wall Street slammed">Reuters</a>)</li>
<li>Between 8–9 a.m. ET on Nov 20, pre-market updates repeatedly showed NVDA up 5%+, dragging Nasdaq futures higher (Nasdaq futures >+1.5%). (<a href="https://www.reuters.com/world/china/global-markets-trading-day-graphic-2025-11-20/?utm_source=chatgpt.com" title="Trading Day: Nvidia sparkle vanishes, Wall Street slammed">Reuters</a>)</li>
<li>08:30 a.m. ET — Jobs report and claims drop. Markets digest a “no quick rate cuts, but no collapse either” narrative. Nvidia remains elevated into the open; there is no evidence of a dump before 8:30. (<a href="https://www.bls.gov/news.release/empsit.nr0.htm?utm_source=chatgpt.com" title="Employment Situation Summary - 2025 M09 Results">Bureau of Labor Statistics</a>)</li>
</ul>
<hr>
<h3>Thursday cash session (20 Nov)</h3>
<ul>
<li>09:30 a.m. ET — NVDA opens $195.95, ticks $196.00 near the open. Indices gap up hard: Dow +700, Nasdaq +2.5% within minutes. (<a href="https://www.investopedia.com/dow-jones-today-11202025-11853411?utm_source=chatgpt.com" title="Markets News, Nov. 20, 2025: Major Stock Indexes Post ...">Investopedia</a>)</li>
<li>Late morning (≈10:30–10:45 a.m.) — Nvidia and the indices roll over. The S&#x26;P 500 and Nasdaq slide from early gains of ~2% to losses over 1.5%, a rare intraday reversal more typical of stress days.</li>
<li>Low: NVDA hits $179.85, an ~8.2% drop from the $196 high.</li>
<li>Close: $180.64, down ~3.1% on the day and ~7.8% below the pre-market high. (<a href="https://stockanalysis.com/stocks/nvda/history/?utm_source=chatgpt.com" title="NVIDIA (NVDA) Stock Price History 1999-2025">StockAnalysis</a>)</li>
</ul>
Market color during the slide focused on profit-taking in a crowded AI leader, options-driven hedging (dealers short gamma selling into weakness), and a reassessment of how much to pay for a stock that had already doubled on the AI story. There were no major new headlines driving the 10:30–4:00 tape.
<hr>
<h3>Friday cash session (21 Nov)</h3>
<ul>
<li>Open: $181.24, a touch above Thursday’s close.</li>
<li>Range: $184.56 high to $172.93 low.</li>
<li>Close: $178.88, about -1% on the day and ~11.8% below Thursday’s high. (<a href="https://stockanalysis.com/stocks/nvda/history/?utm_source=chatgpt.com" title="NVIDIA (NVDA) Stock Price History 1999-2025">StockAnalysis</a>)</li>
</ul>
The broader market stabilized and even finished green, helped late by Fed commentary. Nvidia stayed weak, pointing to stock-specific positioning rather than fresh macro shock.
<hr>
<h2>5. Why people think “it started before the jobs report”</h2>
Two mundane issues create the illusion of pre-data selling:
<ol>
<li>
Time zones. Economic calendars quote 08:30 a.m. ET; many charts default to UTC. 08:30 ET = 13:30 UTC. If you compare a UTC-stamped calendar to an ET-stamped chart, it can look like the stock moved first when you are staring at the same moment in two clocks. (<a href="https://www.bls.gov/news.release/empsit.nr0.htm?utm_source=chatgpt.com" title="Employment Situation Summary - 2025 M09 Results">Bureau of Labor Statistics</a>)
</li>
<li>
Session coverage. Many retail charts show only the 9:30–16:00 ET cash session. The first reactions to both the earnings release (after hours) and the jobs data (pre-market) happen before those charts even draw a candle. By 9:30, the info is already reflected; what you see next is the positioning unwind.
</li>
<li>
Instrument mix. Futures and FX trade almost 24/5; single-stock pre-market liquidity is thinner and cash opens later. Overlaying futures with cash equities without adjusting time windows can create false leads/lags.
</li>
</ol>
Once you align clocks and sessions, the sequence looks straightforward: earnings pop → jobs data digested → late-morning unwind in a crowded trade.
<hr>
<h2>6. What actually drove the reversal</h2>
Beyond the calendar mechanics, the drivers were classic:
<ul>
<li>Crowded positioning. Nvidia was the flagship AI trade, the world’s most valuable listed company, and a core holding in AI-focused ETFs. Even “great” numbers can trigger “good enough to take profits.”</li>
<li>Options and systematic flows. Elevated implied volatility around earnings + macro means dealers hedging short gamma can sell stock as it drops, reinforcing the move. Stop-loss orders and CTA/vol-targeting rules can add momentum.</li>
<li>Valuation check. After doubling (and then some) on the AI story, investors reassessed how much they would pay in a session that started euphoric and flipped to risk-off.</li>
<li>No fresh negative headline. Media coverage at the time explicitly noted the reversal came with “no major headlines” — it was flow- and positioning-driven. (<a href="https://www.investopedia.com/dow-jones-today-11202025-11853411?utm_source=chatgpt.com" title="Markets News, Nov. 20, 2025: Major Stock Indexes Post ...">Investopedia</a>)</li>
</ul>
<hr>
<h2>7. Is there evidence of manipulation?</h2>
To move from “ugly tape” to “illegal manipulation,” you’d expect specifics: non-public access to the jobs report before 8:30, documented spoofing/layering patterns, or regulator/exchange notices flagging abnormal NVDA trading. (<a href="https://www.bls.gov/news.release/empsit.nr0.htm?utm_source=chatgpt.com" title="Employment Situation Summary - 2025 M09 Results">Bureau of Labor Statistics</a>)
Based on public information, none of those exist for 19–21 November 2025, despite intense media attention. Major outlets explained the move with standard factors: positioning, valuations, AI-bubble concerns, and options hedging. (<a href="https://www.reuters.com/world/china/global-markets-trading-day-graphic-2025-11-20/?utm_source=chatgpt.com" title="Trading Day: Nvidia sparkle vanishes, Wall Street slammed">Reuters</a>)
Conclusion: The price path is consistent with a crowded trade being unwound into an earnings + macro window, not a smoking gun of collusion.
<hr>
<h2>8. Practical takeaways</h2>
<ul>
<li>Don’t anchor on the close. “Nvidia down 3%” misses an 8% intraday swing and an ~12% peak-to-trough over two sessions.</li>
<li>Great fundamentals can still be “sell the news.” When expectations and ownership are stretched, excellent prints can be liquidity events for de-risking.</li>
<li>Align clocks and sessions. Check time zones and whether charts include pre-/post-market before inferring causality.</li>
<li>Expect big moves around stacked catalysts. Earnings + delayed macro + crowded positioning + elevated vol is a recipe for two-sided 5–10% swings, even in mega-caps.</li>
<li>Assume flow before conspiracy. In the absence of regulatory flags, the base case is positioning and hedging mechanics.</li>
</ul>
<hr>
Standard disclaimer: this article is for informational and educational purposes only and is not investment advice or a recommendation to buy or sell any security.

Nvidia stock ripped on record Q3 FY26 earnings, then whipsawed nearly 12% from Nov 19-21, 2025. This post lines up the exact trading timeline with the delayed jobs report, futures action, and positioning to show why the move looked ugly but not rigged — plus practical lessons for traders.

Nvidia's November 2025 Flash Crash: Did Wall Street Rig NVDA or Was It Just a Crowded Trade Unwinding?


# Is Gmail Training AI on Your Emails? What's Really Happening (And How to Lock It Down)

## TL;DR: The short answer

* **Gmail absolutely scans your emails with AI and machine learning** for spam filtering, security, and smart features like Smart Compose and email categorization. ([Privacy International][1])
* **According to Google's own documentation,** your Gmail/Workspace data is **not used to train the underlying generative AI models** that power Gemini and Search, unless you explicitly share data or give permission. ([Google Workspace][2])
* **Your Gmail content can be used to improve Gmail's own smart features** (spam filters, Smart Compose, etc.) when certain smart features settings are turned on. That is still AI training, just for Gmail's own models. ([Google Help][3])
* **New Gemini features (like Deep Research) can use Gmail, Drive, and Chat as context** to answer questions, but Google says this contextual use is separate from training the base models. ([Fox News][4])
* You can **significantly reduce how much of your email is used to train or improve AI** by turning off:
  * Smart features in Gmail, Chat, and Meet
  * Smart features in Google Workspace and in other Google products
  * Gemini Apps Activity / "Help improve Google AI"
* You **cannot stop Gmail scanning for spam and security** without leaving Gmail entirely.

This article walks through the "why," "how," and exact settings to change.

## 1. Why Gmail uses AI on your emails at all

Gmail has always relied on machine learning and AI-style systems for three main reasons:

1. **Security and spam filtering**
   * Gmail uses AI-enhanced spam and phishing detection to block a huge volume of junk and malicious emails before they reach your inbox. ([Google Workspace][5])
   * This requires scanning **message content, metadata, links, and attachments** to decide what is safe and what is spam.

2. **Convenience and smart features**
   When you enable smart features, Gmail uses your email content to power things like: ([Google Help][3])
   * Smart Compose (inline text suggestions)
   * Smart Reply (suggested one-tap replies)
   * Primary/Social/Promotions auto-categorization
   * Travel and package summary cards above emails
   * Events auto-added to Calendar, tickets into Wallet, etc.
   Google's own help pages say that when these settings are on, **your Workspace content and activity may also be processed to improve these features**. That is a polite way of saying "used to train or refine the models that power them." ([Google Help][3])

3. **New generative AI features (Gemini in Gmail)**
   * If you have Google AI/Gemini features in Workspace or Gmail, Gemini can **summarize threads, draft replies, and find info in your Workspace content**. ([Google Help][6])
   * For paying Workspace AI and Google One AI plans, Gemini models operate inside Workspace with additional privacy guarantees.

So yes, **AI is deeply baked into Gmail**, but different layers of AI use your emails in different ways.

## 2. Does Gmail actually train AI on your emails?

There are three separate questions here.

### 2.1 Training for spam filters and smart features

This is the part almost nobody argues about.

Google's own smart-features help page says that if you turn on any smart-features settings, your "Workspace content and activity" can be processed **to improve those features** and that these learnings may persist even after you turn the settings off. ([Google Help][3])

That is **AI training**, just not the flashy "big Gemini model" kind. It is training and refining:

* Spam and phishing detection
* Email categorization
* Smart Compose / Smart Reply
* Workspace search suggestions, etc.

So: **if smart features are on, Gmail is absolutely feeding your emails into machine-learning systems to improve those features over time.**

### 2.2 Training the big generative models (Gemini, Search, etc.)

This is where the controversy exploded in late 2025.

Headlines and viral posts claimed that "Google is using your Gmail to train Gemini" and that users were auto-opted in. Fact-checks and follow-up reporting found something more nuanced: ([The Verge][7])

* Google and Workspace privacy pages say **Workspace data (including Gmail) is not used to train or improve the underlying generative AI models that power Gemini and Search without permission.** ([Google Workspace][2])
* A specific Gmail+Gemini help page adds that **Gemini in Gmail/Workspace uses your content to answer prompts but does not use that content to train or improve Gemini or other generative AI models**, and does not store prompts or outputs without your permission. ([Google Help][6])
* Multiple outlets (Verge, Android Central, Snopes, etc.) summarize Google's position the same way: Gmail content itself is not fed into Gemini's base model training pipeline, even though ML is used heavily for Gmail's own features. ([The Verge][7])

So: **according to Google's own docs and public statements, your Gmail messages are not used to train the core Gemini models or Search ranking models by default.**

You might or might not fully trust that, but that is the official position.

### 2.3 What about Gemini Deep Research "scanning" Gmail, Drive, and Chat?

In November 2025, Google announced **Gemini Deep Research can use context from Gmail, Drive, and Chat** to answer questions and perform research-style tasks. ([Fox News][4])

Key points:

* Deep Research can **read your emails and files as context at inference time** to generate answers.
* Coverage notes that Google says **using this context does not mean your Gmail content is automatically added to Gemini's training data**, and that core Gmail content still is not used to train Gemini unless explicitly shared with the AI. ([Fox News][4])

However:

* If you use **Gemini Apps** (the general Gemini web/mobile/chat experience) and explicitly give it data by pasting email content, uploading files, or sharing your screen, **that data is treated like any other Gemini prompt.**
* The Gemini Apps Privacy Hub states that Gemini **uses your activity to provide, maintain, and improve its services, including training generative AI models**, unless you change Gemini Apps Activity settings. ([Google Help][8])

So the realistic picture:

* **Gmail always uses ML on your mail for security and smart features.**
* **Generative AI (Gemini) can now read Gmail content as context to help you, but, per Google, that context is not directly thrown into base model training.**
* Any time you **explicitly share Gmail content with Gemini Apps**, it is treated like normal Gemini prompts and may be used for training unless you opt out in Gemini settings.

## 3. How to stop Gmail and Google AI from using your emails as much as possible

You cannot make Gmail stop scanning for spam and security. But you can:

* Disable or restrict smart features
* Limit how much data can be used to "improve" AI models
* Block Gemini Apps from using your activity for training
* Avoid feeding sensitive content into generative AI at all

### 3.1 Turn off "Smart features in Gmail, Chat, and Meet"

This reduces how much of your email is used to personalize and improve Gmail's AI-powered features.

On desktop Gmail: ([Google Help][3])

1. Open Gmail.
2. Click the gear icon -> "See all settings."
3. In the **General** tab, scroll to **"Smart features in Gmail, Chat and Meet."**
4. Uncheck the box or turn this setting off.
5. Scroll down and click **Save changes**.

On mobile (Android/iOS):

1. Open the Gmail app.
2. Menu -> **Settings** -> choose your account.
3. Under **General**, find **"Smart features."**
4. Uncheck or toggle off.

What you lose:

* Smart Compose and Smart Reply in Gmail
* Automatic Primary/Social/Promotions categorization
* Summary cards like package tracking, travel cards, etc.

What you gain:

* Your Gmail, Chat, and Meet content is no longer used to drive or improve these smart features going forward. Existing learnings may persist, but new data will not be used for improvement once the setting is off. ([Google Help][3])

### 3.2 Turn off "Smart features in Google Workspace" and "Smart features in other Google products"

This is the second layer many people miss. It controls how your Gmail/Workspace data flows into other apps and AI features across Google. ([Google Help][3])

In the Gmail app or on the web:

1. Open Gmail -> Settings.
2. Look for **"Google Workspace smart features"** or a link like **"Manage Workspace smart feature settings."**
3. You should see two options:
   * **Smart features in Google Workspace**
   * **Smart features in other Google products**
4. Turn **both** off.
5. Save if prompted.

What this shuts down:

* Workspace-wide smart features that use Gmail content, such as:
  * Events pulled from Gmail into Calendar
  * Personalized search in Drive based on email/file activity
  * Gemini features inside Workspace that cross-reference Gmail, Drive, Calendar, etc. ([Google Help][3])
* Smart features in other Google products, including:
  * Reservations and tickets pulled into Maps and Wallet
  * Suggestions and answers in the Gemini app based on Workspace content ([Google Help][3])

The same help page makes clear that when any smart-feature setting is on, Workspace content and activity **may be processed to improve those features**. Turning these off stops future use of your mail for that improvement. ([Google Help][3])

### 3.3 Lock down Gemini Apps Activity and training

Even if you lock down Gmail's own settings, Gemini Apps itself can still use what you explicitly give it.

From the **Gemini Apps Privacy Hub**: your prompts, file uploads, and connected-app data can be used to **maintain and improve Gemini, including training its generative models**, and some of that data is reviewed by humans. ([Google Help][8])

To reduce that:

1. Go to **Gemini Apps Activity** (via your Google Account or myactivity.google.com). ([Google Help][8])
2. Turn **Gemini Apps Activity** off, or:
   * Set auto-delete to the shortest interval (for example, 3 months).
   * Explicitly disable the option that lets Google use your activity to improve Google AI, where available.
3. Manually delete past Gemini activity that might contain sensitive email content.

Important nuance: even with this off, Google says some anonymized processing of chats can still be used to improve safety and basic systems, but training of the main models on your identifiable activity is controlled by this setting. ([Google Help][8])

### 3.4 Disable or limit Gemini Deep Research access to Gmail/Drive/Chat

Gemini Deep Research can now pull context from Gmail, Drive, and Chat. Reports note that you can disable this from Google AI/Workspace settings so that Deep Research cannot scan those sources. ([Fox News][4])

Actions to take:

* In **Google Workspace or account-level Gemini settings**, look for controls that enable Gemini access to Gmail, Drive, and Chat and toggle them off where possible.
* Do not enable Labs or experimental Deep Research features that explicitly mention Gmail/Drive context if you want to avoid this entirely.

### 3.5 General Google account privacy tightening

Beyond Gmail-specific toggles, you should:

* Review your **Google Privacy Checkup** and:
  * Turn off or limit **Web & App Activity**.
  * Turn off **Ad personalization**.
  * Consider turning off **Location History**, if not needed. ([Google Transparency][9])

This does not stop Gmail's core spam filtering, but it limits how broadly Google can cross-link your behavior across services.

## 4. Can you completely stop Gmail from scanning your emails?

Short answer: **no, not if you keep using Gmail.**

Even with every smart feature and AI add-on disabled:

* Gmail must still **scan emails for spam, phishing, malware, and abuse**.
* That scanning uses heuristics and machine learning. ([Google Workspace][5])

If you want:

* End-to-end encryption
* Minimal server-side processing
* No mass model-training on content

...you need to consider **a different provider and/or end-to-end encrypted tools**, and possibly host your own mail or use services designed around strong encryption and limited metadata retention.

But for many people, turning off smart features and Gemini training is a pragmatic middle ground: **you keep Gmail's basic security but significantly reduce how much of your email is used to improve AI systems.**

## 5. Practical privacy habits for any email provider

Regardless of how you configure Gmail:

1. **Do not treat email as a vault for secrets.** Avoid storing raw passwords, highly sensitive financial data, or confidential documents directly in your inbox.
2. **Use other channels for truly sensitive content.** For medical, legal, or highly confidential work, consider end-to-end encrypted communication or encrypted file exchange rather than plain email.
3. **Minimize what you feed to generative AI.** Even if policies say data is anonymized, do not paste entire confidential threads into Gemini, ChatGPT, or any other chatbot unless you are comfortable with the associated risks.
4. **Segment your accounts.** Use one email account for "AI-heavy convenience" (smart features on) and a separate, more locked-down account (smart features off) for critical communications if you must stay within Gmail.

Bottom line: Gmail always runs ML for security and convenience, but you can shut off most training-heavy features, limit Gemini’s use of your data, and keep sensitive content out of any generative AI workflow.

---

* [The Verge](https://www.theverge.com/news/826902/gmail-ai-training-data-opt-out)
* [Android Central](https://www.androidcentral.com/apps-software/why-you-should-care-about-what-companies-are-doing-with-your-clicks)

[1]: https://privacyinternational.org/examples/2170/google-ceases-scanning-email-ad-targeting "Google ceases scanning email for ad targeting"
[2]: https://workspace.google.com/blog/identity-and-security/protecting-your-data-era-generative-ai "Data privacy protections with Duet AI in Google Workspace"
[3]: https://support.google.com/mail/answer/15604322?co=GENIE.Platform%3DAndroid&hl=en "Learn about smart features and controls for Google Workspace and other Google products - Android - Gmail Help"
[4]: https://www.foxnews.com/tech/how-stop-google-ai-from-scanning-your-gmail "Google Gemini now scans Gmail messages - here's how to disable it | Fox News"
[5]: https://workspace.google.com/blog/identity-and-security/an-overview-of-gmails-spam-filters "An overview of Gmail's spam filters"
[6]: https://support.google.com/mail/answer/14615114?hl=en "Learn how Gemini in Gmail, Calendar, Chat, Docs, Drive, Sheets, Slides, Meet and Vids protects your data - Gmail Help"
[7]: https://www.theverge.com/news/826902/gmail-ai-training-data-opt-out "Google denies 'misleading' reports of Gmail using your emails to train AI"
[8]: https://support.google.com/gemini/answer/13594961?hl=en "Gemini Apps Privacy Hub - Gemini Apps Help"
[9]: https://transparency.google/intl/en_us/our-policies/privacy-policy-terms-of-service/ "Privacy Policy, Terms of Service, and AI - Transparency Center"


Does Gmail use your emails to train AI like Google Gemini? This guide explains how Gmail's AI works, what "smart features" really do, and step-by-step how to stop Gmail and Google AI from using your emails wherever possible.

Is Gmail Training AI on Your Emails? What's Really Happening (And How to Lock It Down)

SynthID in 2025: Where Google’s Invisible Watermark Shows Up (and Where It Doesn’t)


Paul Graham's essays look disarmingly simple: plain language, drifting structure, and then a line that suddenly clicks everything into place. Under the surface, though, he is following a very specific recipe: surprising question x exploratory drafting x brutal rewriting, all aimed at pieces that are "true, important, new, useful." Think of this less as a slogan and more as a workflow you can steal. As a writing coach, I'll walk you through that workflow in a way you can actually apply to your own drafts.

For a quick mental picture, imagine his process as three intertwined moves rather than separate stages. Surprising questions give the essay its energy, exploratory drafts generate unexpected ideas on the page, and brutal rewriting refines both the thinking and the sound of the sentences. Put differently: you collect what surprises you, ask one real question, write to discover what you think, then revise hard until your ideas and your prose finally match.

**Step 1 - Collect your surprises and obsessions.** Graham's best essays usually begin not from grand topics like "education" or "technology" but from little feelings that something is off: why school essays feel fake, why some programmers are wildly more productive, why certain kinds of work feels meaningful and others deadening. Your job is to treat those irritations and fascinations as raw material, jotting them into notebooks or files whenever they pop up. The more faithfully you collect these anomalies, the easier it becomes to spot questions that haven't already been answered to death.

**Step 2 - Turn a topic into a question.** Instead of thinking "I should write about school," you ask "Why does so much school writing feel dead?" or "What's actually going on when students fake essays?" In "The Age of the Essay," Graham sums this up by saying that an essay is something you write "to try to figure something out," not to dress up a conclusion you already have. Framing your piece as a question gives it direction without locking you into a pre-decided answer, which in turn makes it easier to stay honest when the draft surprises you.

**Step 3 - Write a fast, bad version one.** At this point you sit down and pour out a rough draft as quickly as you can, with zero expectation that it will be coherent or persuasive. In his short piece "Writing, Briefly," Graham describes spending more time on rewriting than on the initial pass, treating the first draft mainly as a way to drag half-formed thoughts into the open where he can see them. You should expect ill-fitting examples, shaky claims, and detours that go nowhere; the goal of version one is simply to get wet clay onto the table so you have something to shape.

**Step 4 - Let the draft meander by interest.** As you draft, you follow the most interesting thought from one paragraph to the next, even if it does not match the outline you imagined in your head. In his essays on the form, Graham argues that this kind of wandering is a feature of real inquiry: if you truly follow the question, the path will bend. When you hit a dead end, you back up and try a different branch, trusting that you can clean up the path later in revision.

**Step 5 - Extract what's true, important, and new.** Once that messy draft exists, you switch into treasure-hunt mode and start looking for the few parts that are actually worth keeping. Here it helps to keep three questions in front of you: what in here genuinely surprised me, what would a smart reader actually care about, and what could only you have written in this particular way. In "How to Write Usefully," Graham defines useful writing as telling people something "true and important" that they didn't already know, and says the job is to say it as unequivocally as possible. Those criteria are demanding, but they are also a powerful filter for trimming away fluff.

**Step 6 - Rewrite in loops.** This is the brutal part, and it is where most of the magic happens. On each pass you try to read your own prose as a skeptical, slightly impatient stranger, watching for two kinds of trouble: boredom, where your attention drifts, and friction, where your claims do not really convince you. Drawing on his essay "Putting Ideas into Words," Graham's view is that if a sentence sounds wrong in your ear, it usually means the thought underneath is still half-baked, so you fix the idea rather than just adding adjectives. A useful quick pass is: cut anything that bores you, rethink anything that feels fuzzy, and simplify any sentence you would be embarrassed to say out loud.

Part of this looping rewrite is making the language sound like an actual person. In "Write Like You Talk," Graham urges writers to "write in the same language you'd use if you were talking to a friend," on the grounds that this is both clearer and harder to fake. As a quick check, read a paragraph aloud and ask, "Would I really say this to someone I respect?" If not, you keep tightening until the sentences sound like a calm, thoughtful version of your real voice rather than a committee memo.

**Step 7 - Use external readers as a reality check.** At some point you have taken the draft as far as you can alone, and your private sense of what works stops being trustworthy. This is when you hand it to one or two smart friends and ask very specific questions: where did you get bored, where did you get lost, and which one sentence do you think I'll most regret later. Graham thanks readers on many of his essays and has described using them exactly this way, not to rubber-stamp his ideas but to locate blind spots he can no longer see. When two or more readers highlight the same weak spot, that almost always marks a real problem in either the idea or the explanation.

**Step 8 - Do a final shaping pass.** By now you know what the essay is really about, so you can prune the wandering path into something more direct while keeping the feeling of live thinking. Graham often keeps his digressions in the piece but moves them into footnotes, letting the main line of argument run cleanly from question to insight. On this pass you also watch for repetition, overly sharp lines you might regret, and side-ideas that deserve their own essays rather than a rushed paragraph here.

**Step 9 - Publish and feed the loop.** When he posts an essay on his site, Graham is not treating publication as the finish line; he is throwing the idea into the world to see what questions, objections, and misunderstandings come back. Over the years, those reactions have helped shape later essays and even startup-investing ideas, turning the whole archive into a record of thinking in public. For you, the venue might be a blog, newsletter, or shared document, but the principle is the same: once real readers engage, they generate the next round of surprises to toss back into your notebook.

Stepping back, most parts of this loop are strongly supported by what writing teachers and cognitive scientists know about how people think. Using writing to clarify thought, drafting quickly before your internal critic shuts you down, revising multiple times, and seeking feedback from real readers are all standard best practices, not just one hacker-investor's quirks. Treating essays as ways to figure things out rather than as performances to prove what you already know is also in line with research on learning by explanation: trying to teach someone else forces you to sharpen your own understanding.

The catch is that some of Graham's specific habits are tools to try, not commandments. Letting a draft meander freely works well if you are willing to cut ruthlessly later, but for newer writers it can turn into pages of unfocused wandering. Skipping any outline at all may suit him, yet many people benefit from a loose scaffold - a few anchor points or section headings - that they later feel free to rearrange. The right way to use this process is as a laboratory: adopt the moves that make your thinking sharper and discard the ones that consistently leave you stuck.

Here's what to avoid if you want to write in this spirit. Avoid starting from a thesis you feel you must defend at all costs; that makes you a lawyer, not an investigator, and it tempts you to ignore inconvenient evidence. Avoid writing to sound impressive rather than clear, a trap Graham warns about when he urges people to write as they talk. And avoid treating rewriting as cosmetic, tweaking only words and punctuation while leaving shaky ideas untouched, because in his system the whole point of revision is to rethink what you are saying, not just how you say it.

Knowing when to ask for help is part of the craft. If you have been through several rewrite loops and still cannot say what your essay is really about in one clear sentence, that is a sign you need a reader or a coach to ask blunt questions. If you keep avoiding your own draft because it feels boring or muddled, go back to your list of surprises and obsessions and see whether you chose a question that genuinely interests you. Outside eyes can help, but the process still starts with your own curiosity.

Bottom line, Paul Graham's method is a disciplined way to turn curiosity into useful essays: collect the anomalies that bother you, turn them into real questions, draft quickly to see what you actually think, revise in loops until ideas and sentences click, then publish and let readers feed you the next round of puzzles. Do that enough times and your essays stop being school-style performances and become tools for learning in public. In the process, you not only write better; you train yourself to notice more, think more clearly, and share what you find in a way that genuinely helps other people.


Paul Graham's essays look disarmingly simple: plain language, drifting structure, and then a line that suddenly clicks everything into place. Under the surface, though, he is following a very specific recipe: surprising question x exploratory drafting x brutal rewriting, all aimed at pieces that are "true, important, new, useful." Think of this less as a slogan and more as a workflow you can steal. As a writing coach, I'll walk you through that workflow in a way you can actually apply to your own drafts.
For a quick mental picture, imagine his process as three intertwined moves rather than separate stages. Surprising questions give the essay its energy, exploratory drafts generate unexpected ideas on the page, and brutal rewriting refines both the thinking and the sound of the sentences. Put differently: you collect what surprises you, ask one real question, write to discover what you think, then revise hard until your ideas and your prose finally match.
Step 1 - Collect your surprises and obsessions. Graham's best essays usually begin not from grand topics like "education" or "technology" but from little feelings that something is off: why school essays feel fake, why some programmers are wildly more productive, why certain kinds of work feels meaningful and others deadening. Your job is to treat those irritations and fascinations as raw material, jotting them into notebooks or files whenever they pop up. The more faithfully you collect these anomalies, the easier it becomes to spot questions that haven't already been answered to death.
Step 2 - Turn a topic into a question. Instead of thinking "I should write about school," you ask "Why does so much school writing feel dead?" or "What's actually going on when students fake essays?" In "The Age of the Essay," Graham sums this up by saying that an essay is something you write "to try to figure something out," not to dress up a conclusion you already have. Framing your piece as a question gives it direction without locking you into a pre-decided answer, which in turn makes it easier to stay honest when the draft surprises you.
Step 3 - Write a fast, bad version one. At this point you sit down and pour out a rough draft as quickly as you can, with zero expectation that it will be coherent or persuasive. In his short piece "Writing, Briefly," Graham describes spending more time on rewriting than on the initial pass, treating the first draft mainly as a way to drag half-formed thoughts into the open where he can see them. You should expect ill-fitting examples, shaky claims, and detours that go nowhere; the goal of version one is simply to get wet clay onto the table so you have something to shape.
Step 4 - Let the draft meander by interest. As you draft, you follow the most interesting thought from one paragraph to the next, even if it does not match the outline you imagined in your head. In his essays on the form, Graham argues that this kind of wandering is a feature of real inquiry: if you truly follow the question, the path will bend. When you hit a dead end, you back up and try a different branch, trusting that you can clean up the path later in revision.
Step 5 - Extract what's true, important, and new. Once that messy draft exists, you switch into treasure-hunt mode and start looking for the few parts that are actually worth keeping. Here it helps to keep three questions in front of you: what in here genuinely surprised me, what would a smart reader actually care about, and what could only you have written in this particular way. In "How to Write Usefully," Graham defines useful writing as telling people something "true and important" that they didn't already know, and says the job is to say it as unequivocally as possible. Those criteria are demanding, but they are also a powerful filter for trimming away fluff.
Step 6 - Rewrite in loops. This is the brutal part, and it is where most of the magic happens. On each pass you try to read your own prose as a skeptical, slightly impatient stranger, watching for two kinds of trouble: boredom, where your attention drifts, and friction, where your claims do not really convince you. Drawing on his essay "Putting Ideas into Words," Graham's view is that if a sentence sounds wrong in your ear, it usually means the thought underneath is still half-baked, so you fix the idea rather than just adding adjectives. A useful quick pass is: cut anything that bores you, rethink anything that feels fuzzy, and simplify any sentence you would be embarrassed to say out loud.
Part of this looping rewrite is making the language sound like an actual person. In "Write Like You Talk," Graham urges writers to "write in the same language you'd use if you were talking to a friend," on the grounds that this is both clearer and harder to fake. As a quick check, read a paragraph aloud and ask, "Would I really say this to someone I respect?" If not, you keep tightening until the sentences sound like a calm, thoughtful version of your real voice rather than a committee memo.
Step 7 - Use external readers as a reality check. At some point you have taken the draft as far as you can alone, and your private sense of what works stops being trustworthy. This is when you hand it to one or two smart friends and ask very specific questions: where did you get bored, where did you get lost, and which one sentence do you think I'll most regret later. Graham thanks readers on many of his essays and has described using them exactly this way, not to rubber-stamp his ideas but to locate blind spots he can no longer see. When two or more readers highlight the same weak spot, that almost always marks a real problem in either the idea or the explanation.
Step 8 - Do a final shaping pass. By now you know what the essay is really about, so you can prune the wandering path into something more direct while keeping the feeling of live thinking. Graham often keeps his digressions in the piece but moves them into footnotes, letting the main line of argument run cleanly from question to insight. On this pass you also watch for repetition, overly sharp lines you might regret, and side-ideas that deserve their own essays rather than a rushed paragraph here.
Step 9 - Publish and feed the loop. When he posts an essay on his site, Graham is not treating publication as the finish line; he is throwing the idea into the world to see what questions, objections, and misunderstandings come back. Over the years, those reactions have helped shape later essays and even startup-investing ideas, turning the whole archive into a record of thinking in public. For you, the venue might be a blog, newsletter, or shared document, but the principle is the same: once real readers engage, they generate the next round of surprises to toss back into your notebook.
Stepping back, most parts of this loop are strongly supported by what writing teachers and cognitive scientists know about how people think. Using writing to clarify thought, drafting quickly before your internal critic shuts you down, revising multiple times, and seeking feedback from real readers are all standard best practices, not just one hacker-investor's quirks. Treating essays as ways to figure things out rather than as performances to prove what you already know is also in line with research on learning by explanation: trying to teach someone else forces you to sharpen your own understanding.
The catch is that some of Graham's specific habits are tools to try, not commandments. Letting a draft meander freely works well if you are willing to cut ruthlessly later, but for newer writers it can turn into pages of unfocused wandering. Skipping any outline at all may suit him, yet many people benefit from a loose scaffold - a few anchor points or section headings - that they later feel free to rearrange. The right way to use this process is as a laboratory: adopt the moves that make your thinking sharper and discard the ones that consistently leave you stuck.
Here's what to avoid if you want to write in this spirit. Avoid starting from a thesis you feel you must defend at all costs; that makes you a lawyer, not an investigator, and it tempts you to ignore inconvenient evidence. Avoid writing to sound impressive rather than clear, a trap Graham warns about when he urges people to write as they talk. And avoid treating rewriting as cosmetic, tweaking only words and punctuation while leaving shaky ideas untouched, because in his system the whole point of revision is to rethink what you are saying, not just how you say it.
Knowing when to ask for help is part of the craft. If you have been through several rewrite loops and still cannot say what your essay is really about in one clear sentence, that is a sign you need a reader or a coach to ask blunt questions. If you keep avoiding your own draft because it feels boring or muddled, go back to your list of surprises and obsessions and see whether you chose a question that genuinely interests you. Outside eyes can help, but the process still starts with your own curiosity.
Bottom line, Paul Graham's method is a disciplined way to turn curiosity into useful essays: collect the anomalies that bother you, turn them into real questions, draft quickly to see what you actually think, revise in loops until ideas and sentences click, then publish and let readers feed you the next round of puzzles. Do that enough times and your essays stop being school-style performances and become tools for learning in public. In the process, you not only write better; you train yourself to notice more, think more clearly, and share what you find in a way that genuinely helps other people.

How Paul Graham Actually Writes Essays


What's the relationship between thinking and talking?

The standard picture goes like this: first you think, then you speak. You generate ideas internally, and then you encode them into language for other people. Clean, linear, and wrong.

Watch your own mind closely when you're struggling with something hard: a tough decision, a knotty problem at work, an abstract concept you're trying to grasp. You don't sit there with finished thoughts waiting patiently in a queue to be translated into words. You stumble. You half-form sentences. You argue with yourself. You discover, while trying to say something, that you don't quite know what you mean yet.

That's the interesting part: very often, you don't think first and talk later. You figure out what you think by trying to say it.

Thinking as internalized conversation

When you are really thinking, what actually happens?

For a lot of people, the honest answer is: a running dialogue. You pose questions to yourself. You test explanations. You rephrase. You anticipate objections. The activity feels less like decoding fully formed ideas into words and more like wrestling with messy half-formed sentences until they click.

This isn't just a poetic analogy. A huge amount of what we call "thinking" uses the same machinery as conversation. The difference is mostly where the words go: out into the air, or back into your own head.

We didn't evolve a separate "high-level reasoning module" that just happens to interface with language. Language itself became our main workspace for complex, abstract thought. Instead of building a new system from scratch, evolution leaned on an existing one and pushed it inward. We learned not only to talk to one another, but to talk to ourselves.

How children learn to think out loud

You can see this process in miniature in young children.

Watch a five-year-old doing a puzzle or playing an imaginative game. They often narrate what they're doing: "Okay, this piece goes here... no, that's wrong... maybe the blue one." Psychologists call this "private speech." It looks like chatter, but it plays a real functional role. They are using language to guide their attention, plan actions, and keep track of rules.

Over time, that external monologue goes underground. The child doesn't stop using language to think; they just stop saying as much of it aloud. The voice moves inside. The end result is the inner dialogue many adults experience when they are concentrating.

In other words, for a lot of us, "thinking" is what happens when private speech becomes fast and quiet.

But what about things you can't easily say?

If thinking leans so heavily on words, how do we deal with things that aren't naturally verbal, like rotating a 3D object in your mind, improvising music, or feeling grief?

This is where the first simple story (thinking equals talking) breaks. Not all thought is verbal. We clearly have visual, spatial, auditory, and emotional forms of cognition that don't require full sentences.

However, there's an important pattern:

For immediate perception and skilled action (catching a ball, reading a face, playing a piece you've practiced a thousand times), you mostly don't need language.

For deliberate reasoning about complex or abstract issues (justice, strategy, systems, tradeoffs), you almost always drag in words.

You can feel confused without words. But when you want to debug the confusion, you start to narrate: "What exactly is bothering me here?" As soon as you seriously try to justify, compare, or generalize, you end up talking to yourself if not to others.

Language is not the only form of thought, but it is our default medium for explicit, structured reasoning.

Why explaining things changes your mind

This is why explaining something can suddenly make it clearer, even if you thought you already understood it.

When you explain an idea to someone else, you can't rely on your private shortcuts. Your internal dialogue is allowed to be fragmentary because you already know the missing pieces. The listener doesn't. To make sense to them, you have to:

Fill in the gaps.

Make assumptions explicit.

Order the steps so they actually follow.

Replace fuzzy placeholders ("you know, that thing") with real content.

That process often reveals that your mental model was half-baked. You thought you "knew" it, but it only worked inside the forgiving environment of your own head. By forcing yourself to articulate the idea in a full, public sentence, you either solidify it or expose where it breaks.

Teaching, in that sense, doesn't just communicate understanding; it manufactures it.

Why writing is a thinking tool, not just an output

Writing goes even further.

Speech can be sloppy and ephemeral. You say something, correct yourself mid-sentence, and the previous version disappears. Writing leaves a record. Once a sentence is on the page, you can stare at it and ask:

Is this actually what I mean?

Is there a hidden assumption here?

Does this follow from the previous paragraph?

Would someone else interpret this the way I intend?

When you fail to write something clearly, the usual reason is not lack of literary talent. It's that you haven't finished thinking it through. The problem isn't in the keyboard; it's in the model.

Rewriting isn't "polish" in the cosmetic sense. It is iterative thinking in public. Each draft is a pass at making your internal dialogue more explicit, more precise, and less contradictory.

Training your inner voice

If a lot of your reasoning runs on internal dialogue, then the quality of that dialogue matters.

Two people can have the same raw cognitive horsepower and end up with very different levels of insight simply because one of them has trained a better inner conversational partner. Consider the difference between:

An internal voice that says, "This is probably fine, don't overthink it," versus

One that says, "Spell out what you mean. What's the evidence? What would convince you you're wrong?"

The second person isn't smarter in the IQ-test sense; they just have a more demanding, better-practiced internal critic. They've spent years explaining their own thoughts to themselves, on paper, to other people, and that training shows up as clearer thinking.

The practical implication is simple: you can improve your thinking by deliberately improving how you "talk" about problems.

Concretely:

- When you're stuck, write down what you think is true, then write the objections.
- When something feels off, try explaining the situation to an imaginary colleague who knows nothing about it.
- When you have an intuition, force yourself to state it as a claim and list what would falsify it.

You are upgrading the quality of your inner conversation.

Fragmented communication, fragmented thought

Modern communication tools don't change any of this, but they push on it.

Text messages, group chats, and social feeds reward speed and brevity. They optimize for "reply now" and "fit it in a tiny box," not for building a long chain of reasoning. If most of your expressive life happens in that environment, you may find your thoughts starting to take on the same shape: reactive, clipped, half-finished.

That doesn't mean short form is bad by definition. A tight, 200-character statement can be a model of clarity. The danger is not length; it's habit. If you never practice sustained, careful articulation, you shouldn't expect to have sustained, careful thoughts.

You think the way you train yourself to talk.

The real punchline

The interesting claim is not that thinking and talking are literally the same thing. They aren't. You can have rich, meaningful, nonverbal experience. You can play chess largely in patterns, not sentences.

The real claim is narrower and more useful:

For the kind of complex, explicit reasoning humans care about (plans, arguments, explanations, theories), language is the main workspace.

Your internal dialogue and your external expression are two sides of the same system.

Improving one almost inevitably improves the other.

So better thinking is not a mystical gift reserved for people with exotic brains. It is, to a large extent, the result of a skill you can practice: learning to have better conversations with others, and with yourself.

Talk better to think better. And when in doubt, write.


What's the relationship between thinking and talking?
The standard picture goes like this: first you think, then you speak. You generate ideas internally, and then you encode them into language for other people. Clean, linear, and wrong.
Watch your own mind closely when you're struggling with something hard: a tough decision, a knotty problem at work, an abstract concept you're trying to grasp. You don't sit there with finished thoughts waiting patiently in a queue to be translated into words. You stumble. You half-form sentences. You argue with yourself. You discover, while trying to say something, that you don't quite know what you mean yet.
That's the interesting part: very often, you don't think first and talk later. You figure out what you think by trying to say it.
Thinking as internalized conversation
When you are really thinking, what actually happens?
For a lot of people, the honest answer is: a running dialogue. You pose questions to yourself. You test explanations. You rephrase. You anticipate objections. The activity feels less like decoding fully formed ideas into words and more like wrestling with messy half-formed sentences until they click.
This isn't just a poetic analogy. A huge amount of what we call "thinking" uses the same machinery as conversation. The difference is mostly where the words go: out into the air, or back into your own head.
We didn't evolve a separate "high-level reasoning module" that just happens to interface with language. Language itself became our main workspace for complex, abstract thought. Instead of building a new system from scratch, evolution leaned on an existing one and pushed it inward. We learned not only to talk to one another, but to talk to ourselves.
How children learn to think out loud
You can see this process in miniature in young children.
Watch a five-year-old doing a puzzle or playing an imaginative game. They often narrate what they're doing: "Okay, this piece goes here... no, that's wrong... maybe the blue one." Psychologists call this "private speech." It looks like chatter, but it plays a real functional role. They are using language to guide their attention, plan actions, and keep track of rules.
Over time, that external monologue goes underground. The child doesn't stop using language to think; they just stop saying as much of it aloud. The voice moves inside. The end result is the inner dialogue many adults experience when they are concentrating.
In other words, for a lot of us, "thinking" is what happens when private speech becomes fast and quiet.
But what about things you can't easily say?
If thinking leans so heavily on words, how do we deal with things that aren't naturally verbal, like rotating a 3D object in your mind, improvising music, or feeling grief?
This is where the first simple story (thinking equals talking) breaks. Not all thought is verbal. We clearly have visual, spatial, auditory, and emotional forms of cognition that don't require full sentences.
However, there's an important pattern:
For immediate perception and skilled action (catching a ball, reading a face, playing a piece you've practiced a thousand times), you mostly don't need language.
For deliberate reasoning about complex or abstract issues (justice, strategy, systems, tradeoffs), you almost always drag in words.
You can feel confused without words. But when you want to debug the confusion, you start to narrate: "What exactly is bothering me here?" As soon as you seriously try to justify, compare, or generalize, you end up talking to yourself if not to others.
Language is not the only form of thought, but it is our default medium for explicit, structured reasoning.
Why explaining things changes your mind
This is why explaining something can suddenly make it clearer, even if you thought you already understood it.
When you explain an idea to someone else, you can't rely on your private shortcuts. Your internal dialogue is allowed to be fragmentary because you already know the missing pieces. The listener doesn't. To make sense to them, you have to:
Fill in the gaps.
Make assumptions explicit.
Order the steps so they actually follow.
Replace fuzzy placeholders ("you know, that thing") with real content.
That process often reveals that your mental model was half-baked. You thought you "knew" it, but it only worked inside the forgiving environment of your own head. By forcing yourself to articulate the idea in a full, public sentence, you either solidify it or expose where it breaks.
Teaching, in that sense, doesn't just communicate understanding; it manufactures it.
Why writing is a thinking tool, not just an output
Writing goes even further.
Speech can be sloppy and ephemeral. You say something, correct yourself mid-sentence, and the previous version disappears. Writing leaves a record. Once a sentence is on the page, you can stare at it and ask:
Is this actually what I mean?
Is there a hidden assumption here?
Does this follow from the previous paragraph?
Would someone else interpret this the way I intend?
When you fail to write something clearly, the usual reason is not lack of literary talent. It's that you haven't finished thinking it through. The problem isn't in the keyboard; it's in the model.
Rewriting isn't "polish" in the cosmetic sense. It is iterative thinking in public. Each draft is a pass at making your internal dialogue more explicit, more precise, and less contradictory.
Training your inner voice
If a lot of your reasoning runs on internal dialogue, then the quality of that dialogue matters.
Two people can have the same raw cognitive horsepower and end up with very different levels of insight simply because one of them has trained a better inner conversational partner. Consider the difference between:
An internal voice that says, "This is probably fine, don't overthink it," versus
One that says, "Spell out what you mean. What's the evidence? What would convince you you're wrong?"
The second person isn't smarter in the IQ-test sense; they just have a more demanding, better-practiced internal critic. They've spent years explaining their own thoughts to themselves, on paper, to other people, and that training shows up as clearer thinking.
The practical implication is simple: you can improve your thinking by deliberately improving how you "talk" about problems.
Concretely:
<ul>
<li>When you're stuck, write down what you think is true, then write the objections.</li>
<li>When something feels off, try explaining the situation to an imaginary colleague who knows nothing about it.</li>
<li>When you have an intuition, force yourself to state it as a claim and list what would falsify it.</li>
</ul>
You are upgrading the quality of your inner conversation.
Fragmented communication, fragmented thought
Modern communication tools don't change any of this, but they push on it.
Text messages, group chats, and social feeds reward speed and brevity. They optimize for "reply now" and "fit it in a tiny box," not for building a long chain of reasoning. If most of your expressive life happens in that environment, you may find your thoughts starting to take on the same shape: reactive, clipped, half-finished.
That doesn't mean short form is bad by definition. A tight, 200-character statement can be a model of clarity. The danger is not length; it's habit. If you never practice sustained, careful articulation, you shouldn't expect to have sustained, careful thoughts.
You think the way you train yourself to talk.
The real punchline
The interesting claim is not that thinking and talking are literally the same thing. They aren't. You can have rich, meaningful, nonverbal experience. You can play chess largely in patterns, not sentences.
The real claim is narrower and more useful:
For the kind of complex, explicit reasoning humans care about (plans, arguments, explanations, theories), language is the main workspace.
Your internal dialogue and your external expression are two sides of the same system.
Improving one almost inevitably improves the other.
So better thinking is not a mystical gift reserved for people with exotic brains. It is, to a large extent, the result of a skill you can practice: learning to have better conversations with others, and with yourself.
Talk better to think better. And when in doubt, write.

Why Good Thinking Sounds Like Good Conversation


What does it mean to be human?

Here is the blunt version: you are a fragile animal that knows it is going to die, and you are trying to make that fact bearable.

Everything else is details.

We build religions, write laws, fall in love, write code, start wars, write poems, scroll endlessly on phones, and stare at ceilings at 3 a.m. All of that is one species wrestling with the same basic problem: we are conscious, temporary, and we care.

* * *

#### A body that knows it will end

To be human is first to be a body.

You bleed, you ache, you age. You get hungry and tired and sick. Your heart will stop. That is not poetry; it is a medical certainty.

But unlike other animals, you do not just _experience_ this; you can _anticipate_ it. You can imagine your own death in vivid detail. You can picture the funeral, the empty chair, the messages people will send when you are gone.

This is both a curse and a superpower. Without the awareness of death, there would be no urgency, no bucket lists, no sense that time matters. With it, there is anxiety, denial, and the constant temptation to distract ourselves from the obvious.

Being human means carrying that awareness and still doing the dishes.

* * *

#### A brain that runs simulations

Humans do not just react; we rehearse.

Your brain runs mental simulations all day: what could go wrong, what might happen, what they might say, how you will respond. You replay past conversations and rewrite them; you rehearse future ones that may never happen.

This ability to imagine, to tell stories, is a defining feature of being human. We build entire worlds that exist only in language:

- nations
- companies
- religions
- markets
- money

None of these are physical objects. They exist because enough people agree to treat them as real.

To be human is to live inside stories, not just inside skin.

* * *

#### We become ourselves through others

You do not invent yourself alone.

From birth, you are shaped by other people: parents, siblings, friends, teachers, enemies, strangers online. You learn what is "normal" from the tribe you happen to be born into: what you can say, who you can love, what you should want.

Being human means:

- craving approval and fearing rejection
- constantly reading faces for signals of belonging or threat
- feeling joy or shame because of what other people think, or what you imagine they might think

We like to talk about "being true to yourself," but the self is a social construction. You are, in large part, the reflection of the relationships you have had and the culture you swim in.

To be human is to be entangled.

* * *

#### The moral animal

We are not just clever apes; we are judging apes.

Humans obsess over "should." We argue not only about what _is_ happening, but about what _ought_ to happen:

- Is this fair?
- Is this right?
- Who deserves what?

We will sacrifice money, comfort, even our lives for principles, but we will also twist principles to justify what we wanted to do anyway. We are capable of astonishing generosity and breathtaking cruelty, often in the name of the same moral ideal.

To be human is to live within a permanent tension:

- between empathy and indifference
- between justice and self-interest
- between who we think we are and what we actually do

Our moral sense is not clean; it is complicated, biased, and sometimes hypocritical. Yet without it, we would not recognize ourselves.

* * *

#### Creativity, and the mess that comes with it

Humans make things that do not exist yet.

We paint, compose, design, engineer, theorize. We build tools that make new tools possible. Fire, wheels, vaccines, nuclear weapons, smartphones, machine learning systems: each step extends our power and our reach.

But every tool is double-edged.

The same ingenuity that writes a symphony builds a concentration camp. The same algorithms that connect families spread disinformation and addiction. The same curiosity that unlocks clean energy can also refine more efficient ways to destroy.

To be human is to create beyond our current level of wisdom, and then scramble to catch up.

* * *

#### The search for meaning

Underneath the noise of daily life, there is a quieter question: "What is the point of all this?"

People answer it in many ways:

- religion and faith
- art and beauty
- service and community
- success and status
- family and love
- exploration and discovery

Some lean on ancient traditions; others try to build their own from scratch. Many just try not to think about it too much.

There is no single correct answer. The uncomfortable truth is that meaning is not handed to us; we project it. We decide what matters, and then we live as if that decision is objectively true.

To be human is to participate in this act of meaning-making, consciously or not.

* * *

#### Being human is a verb

So what does it mean to be human?

It is not just belonging to the species Homo sapiens or having a highly developed brain. Those are technicalities. The deeper reality is:

To be human is to be:

- a mortal body that knows it is mortal,
- a mind that tells stories,
- a social being who becomes itself through others,
- a moral agent struggling with its own contradictions,
- a creator of tools and meanings that can outgrow our control.

And crucially, it is not a static state. Being human is something you do every day:

- in the way you treat the people who cannot give you anything
- in what you pay attention to when nobody is watching
- in the stories you choose to believe and pass on
- in how you respond to your own fear, confusion, and desire

We are not just the species that asks, "What does it mean to be human?" We are the species that answers that question with our behavior, collectively, in real time.

The definition is not finished. It is being revised every time we choose to act with courage instead of cowardice, curiosity instead of indifference, honesty instead of comfort.

That, more than any cosmic metaphor, is what it means to be human.


What does it mean to be human?
Here is the blunt version: you are a fragile animal that knows it is going to die, and you are trying to make that fact bearable.
Everything else is details.
We build religions, write laws, fall in love, write code, start wars, write poems, scroll endlessly on phones, and stare at ceilings at 3 a.m. All of that is one species wrestling with the same basic problem: we are conscious, temporary, and we care.
<hr>
<h4>A body that knows it will end</h4>
To be human is first to be a body.
You bleed, you ache, you age. You get hungry and tired and sick. Your heart will stop. That is not poetry; it is a medical certainty.
But unlike other animals, you do not just experience this; you can anticipate it. You can imagine your own death in vivid detail. You can picture the funeral, the empty chair, the messages people will send when you are gone.
This is both a curse and a superpower. Without the awareness of death, there would be no urgency, no bucket lists, no sense that time matters. With it, there is anxiety, denial, and the constant temptation to distract ourselves from the obvious.
Being human means carrying that awareness and still doing the dishes.
<hr>
<h4>A brain that runs simulations</h4>
Humans do not just react; we rehearse.
Your brain runs mental simulations all day: what could go wrong, what might happen, what they might say, how you will respond. You replay past conversations and rewrite them; you rehearse future ones that may never happen.
This ability to imagine, to tell stories, is a defining feature of being human. We build entire worlds that exist only in language:
<ul>
<li>nations</li>
<li>companies</li>
<li>religions</li>
<li>markets</li>
<li>money</li>
</ul>
None of these are physical objects. They exist because enough people agree to treat them as real.
To be human is to live inside stories, not just inside skin.
<hr>
<h4>We become ourselves through others</h4>
You do not invent yourself alone.
From birth, you are shaped by other people: parents, siblings, friends, teachers, enemies, strangers online. You learn what is "normal" from the tribe you happen to be born into: what you can say, who you can love, what you should want.
Being human means:
<ul>
<li>craving approval and fearing rejection</li>
<li>constantly reading faces for signals of belonging or threat</li>
<li>feeling joy or shame because of what other people think, or what you imagine they might think</li>
</ul>
We like to talk about "being true to yourself," but the self is a social construction. You are, in large part, the reflection of the relationships you have had and the culture you swim in.
To be human is to be entangled.
<hr>
<h4>The moral animal</h4>
We are not just clever apes; we are judging apes.
Humans obsess over "should." We argue not only about what is happening, but about what ought to happen:
<ul>
<li>Is this fair?</li>
<li>Is this right?</li>
<li>Who deserves what?</li>
</ul>
We will sacrifice money, comfort, even our lives for principles, but we will also twist principles to justify what we wanted to do anyway. We are capable of astonishing generosity and breathtaking cruelty, often in the name of the same moral ideal.
To be human is to live within a permanent tension:
<ul>
<li>between empathy and indifference</li>
<li>between justice and self-interest</li>
<li>between who we think we are and what we actually do</li>
</ul>
Our moral sense is not clean; it is complicated, biased, and sometimes hypocritical. Yet without it, we would not recognize ourselves.
<hr>
<h4>Creativity, and the mess that comes with it</h4>
Humans make things that do not exist yet.
We paint, compose, design, engineer, theorize. We build tools that make new tools possible. Fire, wheels, vaccines, nuclear weapons, smartphones, machine learning systems: each step extends our power and our reach.
But every tool is double-edged.
The same ingenuity that writes a symphony builds a concentration camp. The same algorithms that connect families spread disinformation and addiction. The same curiosity that unlocks clean energy can also refine more efficient ways to destroy.
To be human is to create beyond our current level of wisdom, and then scramble to catch up.
<hr>
<h4>The search for meaning</h4>
Underneath the noise of daily life, there is a quieter question: "What is the point of all this?"
People answer it in many ways:
<ul>
<li>religion and faith</li>
<li>art and beauty</li>
<li>service and community</li>
<li>success and status</li>
<li>family and love</li>
<li>exploration and discovery</li>
</ul>
Some lean on ancient traditions; others try to build their own from scratch. Many just try not to think about it too much.
There is no single correct answer. The uncomfortable truth is that meaning is not handed to us; we project it. We decide what matters, and then we live as if that decision is objectively true.
To be human is to participate in this act of meaning-making, consciously or not.
<hr>
<h4>Being human is a verb</h4>
So what does it mean to be human?
It is not just belonging to the species Homo sapiens or having a highly developed brain. Those are technicalities. The deeper reality is:
To be human is to be:
<ul>
<li>a mortal body that knows it is mortal,</li>
<li>a mind that tells stories,</li>
<li>a social being who becomes itself through others,</li>
<li>a moral agent struggling with its own contradictions,</li>
<li>a creator of tools and meanings that can outgrow our control.</li>
</ul>
And crucially, it is not a static state. Being human is something you do every day:
<ul>
<li>in the way you treat the people who cannot give you anything</li>
<li>in what you pay attention to when nobody is watching</li>
<li>in the stories you choose to believe and pass on</li>
<li>in how you respond to your own fear, confusion, and desire</li>
</ul>
We are not just the species that asks, "What does it mean to be human?" We are the species that answers that question with our behavior, collectively, in real time.
The definition is not finished. It is being revised every time we choose to act with courage instead of cowardice, curiosity instead of indifference, honesty instead of comfort.
That, more than any cosmic metaphor, is what it means to be human.

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