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Everyone's obsessed with telling AI what to do. Nobody's talking about telling AI what not to do.

Negative prompting is the secret to getting AI to stop doing the annoying things it always does.

What is negative prompting?

Negative prompting is explicitly telling the AI what to avoid, exclude, or never do.

Instead of:

Write a professional email to a client.

Use:

Write a professional email to a client.

DO NOT:

- Use exclamation marks

- Say "I hope this email finds you well"

- Use corporate jargon like "synergy" or "circle back"

- Apologize unnecessarily

- End with "let me know if you have any questions"

The result? An email that sounds like a human wrote it, not a corporate AI.

Why negative prompting works

AI models are trained on the internet. The internet is full of bad writing, clichés, and corporate speak. Without negative prompts, AI defaults to the most common patterns — which are usually the worst ones.

Negative prompting forces the model to avoid those defaults. It's like telling a chef "don't oversalt" before they start cooking. They'll still make something good, but now they're aware of the most common mistake.

Where negative prompting matters most

Content generation: "Don't use buzzwords. Don't write clickbait headlines. Don't start every paragraph with 'In today's world.'"

Image generation: "No watermarks. No text. No distorted faces. No extra limbs." (Negative prompting in Midjourney and Stable Diffusion dramatically improves output quality.)

Code generation: "Don't use deprecated functions. Don't write comments that just restate the code. Don't ignore error handling."

AI personas: "Don't be overly enthusiastic. Don't use emojis. Don't say 'as an AI language model.'"

The patterns worth blocking

AI has tells — phrases and patterns that scream "this was written by AI." Negative prompting removes them:

- "Delve into"

- "It's worth noting that"

- "In conclusion"

- "Leverage" (when "use" works fine)

- "Robust" (when "strong" works fine)

- "Seamless" (when "smooth" works fine)

Block these, and your AI-generated content becomes 10x more human.

How to build a negative prompt library

Every time you see AI output something you don't like, add it to your negative prompt list. Over time, you'll have a personal style guide that makes AI sound like you.

Example negative prompt library for business writing:

NEVER:

- Use exclamation marks

- Start with "I hope this email finds you well"

- End with "please let me know if you have any questions"

- Use "just checking in"

- Apologize without reason

- Use "per my last email"

- Write "as per our conversation"

- Use "at your earliest convenience"

What this means

If you're using AI for content, code, or communication, start building your negative prompt library. It's the fastest way to improve output quality.

If you're building AI products, give users the ability to define negative prompts. Power users will use it to fine-tune outputs to their exact preferences.

If you're evaluating AI tools, check if they support negative prompting. The ones that do are built by people who understand how AI actually behaves.

What's next

Expect negative prompting to become standard practice. The best AI users already do this. The rest will catch up once they realize how much better their outputs get.

Expect AI models to get better at inferring negative prompts. Future models will learn your preferences over time and automatically avoid patterns you don't like.

BTW: The best AI-generated content doesn't sound like AI. That's because the person prompting it spent as much time telling the AI what not to do as what to do.

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The Recap: The AI data center boom is no longer just an electricity problem—it’s a construction and supply-chain problem. With private data center construction spending surging (TechCrunch cites an annualized pace north of $41B), AI facilities are increasingly competing with “normal” infrastructure projects for the same scarce inputs: land, labor, and specialized equipment.

Unpacked:

Bottom line: The AI boom is turning into a real-world resource contest. As hyperscalers spend aggressively to lock in capacity, the hidden cost isn’t just megawatts—it’s opportunity cost across the built environment, where every diverted crew and delayed transformer pushes other infrastructure further back in line.

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Grok spreads misinformation about the Bondi Beach shooting, misidentifying key people and showing why real-time chatbot “news” is a liability.

Netflix bets $82.6B to buy Warner Bros., a consolidation move that drags Hollywood deeper into platform-empire economics.

Thea previews Helios as a software-heavy fusion design, tying the AI infrastructure wave to attempts at radically cheaper energy.

Amazon launches Kindle “Ask this book” AI without author opt-out, signaling permissionless product shifts over creative rights.

Mercor automates hiring end-to-end with LLM screening and interviews, turning labor-market access into software infrastructure.

SpaceX blames Chinese satellite operator for a near-collision, a preview of LEO traffic conflict as constellations multiply.

Rivian goes AI-first on autonomy and platform strategy, showing EV startups increasingly have to compete like software companies to survive.

Best of AI™ Team

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