Welcome, humans.

A reddit post is making the rounds today of a textbook where the author forgot to clean up the ChatGPT output. The dead giveaway: the page ends with the immortal line, "If you want, I can also explain columns, primary keys, or other DBMS terms."

Imagine your history teacher pausing mid-lecture to ask whether the class "wants" the Treaty of Versailles explained. “No, Mr. Green, we don’t want to learn ANY of this. We want to have recess for six hours then go watch League of Legends videos.” 

This is particularly funny to us because previously, textbooks existed precisely because the author already decided what you need to know before you knew you needed it. "Shut up and learn the columns" is what it should actually say. 

TBH, we probably need a cultural reckoning with the length of things. Alec Stapp flagged yesterday that high-status tech people are now posting "3,000-word slop articles that get over 1M views" with zero shame. Same now goes for books?

Ethan Mollick added that historically speaking, word count used to be an indication that someone thought a lot about something and worked really hard to write it (a.k.a: it was valuable). IMO, the opposite is now more or less true. It takes a lot of work to be concise, ppl! 

Here’s what happened in AI today:

…and a whole lot more that you can read about here.

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🙀 Cerebras' IPO will test the limits of the AI compute boom

So in AI, there's this thing called data centers. You probably know about them (maybe you hate them!). But what is a data center? Picture a giant warehouse full of metal racks; each rack packed with specialized chips that crunch the numbers behind every ChatGPT, Claude, or Gemini request. 

And so far, more AI demand = more chips = bigger racks = more and bigger buildings.

Well, the big story today is a chip company called Cerebras. The most interesting thing about them is that, instead of cutting a silicon wafer into hundreds of smaller chips, they make one chip the size of a dinner plate. 

Oh, also OpenAI is paying them $20B+ for 750 megawatts (MW) of inference compute (the aforementioned AI-number crunching) through 2028. 

Well, they're going public this Thursday, and just upsized the IPO. Here's the details:

Here's why that's a big deal: NVIDIA is the most valuable company in the world (well,trading places off and on with Google these days), and Cerebras is a real threat. Why? For inference (the part where an AI generates a reply to your prompt), bigger chips mean less data shuttling between chips, which means faster and cheaper answers. 

NVIDIA agrees so much that last December, it paid $20B to acqui-hire Groq, a rival chip startup (Groq the company, not Grok Elon's chatbot, to be clear). And remember last week, when xAI leased its Colossus 1 facility, all 220,000 NVIDIA chips, to Anthropic for ~$5B a year? Elon is basically a cloud landlord now too. 

Why should YOU care? Cheaper, faster inference = better AI tools at a lower bill. Also, not financial advice and shared for educational purposes only, but public companies are investable by the public, so y'know, there's that.

Demand for data centers is everywhere atm: 

Why aren't MORE people freaking out? Because demand for intelligence might be the closest thing the economy has ever seen to demand for energy: virtually limitless.

There are no low-energy, high-income countries on earth; every wealthy economy runs on enormous amounts of electricity. Anthropic's Dario Amodei argues intelligence is now a basic factor of production, like land, labor, or capital. If he's right, every economy that wants to grow will have to consume vast quantities of it. Today's spending starts looking less like a bubble and more like laying rails for the next industrial revolution.

That unlimited demand also has to live somewhere physical. Stratechery's Ben Thompson, writing about this exact IPO yesterday, splits inference into two types: 

Cerebras is perfectly timed for the first market. The second one looks different, and can run on slower, cheaper, or even orbital compute… wherever electricity is cheapest.

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🎓 AI Skill of the Day: Track how fast ChatGPT and Claude cite your content

For the first time, we have public benchmarks on how long it takes a newly published page to show up as a citation inside ChatGPT or Claude. Josh Blyskal combed through billions of logs plus ~900 freshly published marketing pages and found:

That gives every content and marketing team a real clock. If you're past day 37 without a citation, the problem is almost certainly your setup (robots.txt blocks, missing sitemap entries, page buried too deep), not patience. If you hit a citation in under a week, you're ahead of the curve and should keep doing whatever's working.

Use this prompt to figure out where a specific page stands

Have a specific skill you want to learn? Request it here. 

🍪 Treats to Try

📰 Around the Horn

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🔧 Tuesday Tool Tip: Try OpenCode, the open-source Claude Code alternative

If you've been wanting to try a Claude-Code-style coding agent but balked at getting a Max subscription, OpenCode is the most popular open-source alternative right now: 150K+ GitHub stars, 6.5M monthly developers, and it runs in your terminal, IDE, or desktop (plus, you can run it with whatever model you want… well, EXCEPT Claude). 

Install in 10 seconds: just open your terminal and type: curl -fsSL https://opencode.ai/install | bash (or npm i -g opencode-ai if you prefer)... or give Codex this url and ask it to do it for you: https://opencode.ai/install

Basic flow: run opencode in any project directory, type what you want it to do in, and it reads files, edits code, and runs commands the way Claude Code does. Hit Tab mid-session to swap models (the popular ones are Claude, GPT-5.5, Gemini, DeepSeek, your local AI via Ollama, whatever); your bring your own key (BYOK) for paid models, or use the free ones OpenCode bundles.

TBH, there's no reason to lock yourself to a single vendor right now; play around with whatever you like best!

A Cat’s Commentary

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