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Tuesday, June 03, 2025

Cold Email: Best Practices for Getting Results in 2025



Cold Email: Best Practices for Getting Results in 2025 

Cold emailing remains one of the most effective—yet misunderstood—tools in the modern business toolkit. Whether you’re reaching out to potential clients, investors, journalists, or collaborators, a well-crafted cold email can open doors that were previously closed. But in a world saturated with spam and inbox fatigue, how do you make sure your message gets read?

Here are the best practices for writing cold emails that convert in 2025.

1. Do Your Homework

Generic blasts don’t work. Research your recipient. Understand their company, recent projects, and pain points. Show them you’ve done the work with a personalized first line that proves you’re not just another spammer.

Bad:

“Hey there, I thought you might be interested in our product.”

Good:

“I saw your recent post on sustainable design—impressive work on the Nairobi housing project.”

2. Craft a Killer Subject Line

You have 3 seconds. That’s how long it takes for someone to decide if your email is worth opening. A good subject line is personal, relevant, and curiosity-inducing.

Examples:

  • “Quick idea to boost retention at [Company Name]”

  • “Saw your article—had to reach out”

  • “Intro from someone who knows your pain”

Avoid clickbait. You’ll lose trust immediately.

3. Lead with Value, Not a Pitch

Most cold emails fail because they jump straight into selling. Instead, frame your offer around the value or outcome you can deliver.

Example:

“I help ecommerce brands like yours reduce abandoned carts by 30% using AI-driven remarketing—thought that might be relevant as you scale.”

4. Keep It Short and Sweet

Your recipient is busy. Respect their time.

  • Stick to 3–5 sentences max.

  • Use simple, readable language.

  • Break into short paragraphs for easy scanning.

5. End with a Clear Call to Action (CTA)

Don’t make them guess what you want. End with a low-commitment CTA.

Examples:

  • “Open to a 15-minute call next week?”

  • “Would it make sense to explore this further?”

  • “Can I send over a short case study?”

Avoid vague endings like “Let me know what you think.”

6. Follow Up Without Being Annoying

People are busy—not necessarily disinterested. Follow up 2–3 times over the next 10 days. Keep it polite and value-driven.

Follow-up tip:

“Just bumping this up—worth a look if boosting user engagement is still a priority this quarter.”

7. Use a Professional Signature

Make it easy to verify who you are. Include:

  • Full name

  • Title/company

  • Website or LinkedIn link

  • Contact info

It builds trust and shows you're legit.

8. Test and Iterate

Great cold emailers don’t “set and forget.” They test:

  • Subject lines

  • First sentences

  • CTA phrasing

  • Send times

Track open rates, reply rates, and positive responses. Use tools like Mailtrack, Lemlist, or Instantly to optimize.

9. Avoid These Red Flags

  • Too many links (looks spammy)

  • Large attachments (can trigger filters)

  • Overuse of bold, caps, or exclamation marks

  • Writing like a marketer, not a human

10. Bonus: Let AI Help

In 2025, there’s no excuse for writing from scratch. Use AI tools like ChatGPT to:

  • Generate variations

  • Summarize research on a lead

  • Personalize intros at scale

AI can help you stay human and efficient.

Final Thought

The best cold emails are not sales pitches—they’re the beginning of conversations. When written with empathy, clarity, and value in mind, a cold email can be the warmest path to a new opportunity.

Now go hit send—just do it right.



 

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Online Dating + AI: Fixing Online Dating: Why Better Design Matters More Than Better AI

 

Fixing Online Dating: Why Better Design Matters More Than Better AI

Online dating is broken—not because we lack smart algorithms, but because the foundation has been flawed from the start.

Dating apps have prioritized engagement metrics over real-world outcomes. The revenue models reward addictive swiping, gamified attraction, and fantasy-driven illusions. Women often end up flooded with attention from the top 10% of men, creating an unrealistic marketplace of desire. Men, meanwhile, are stuck in message limbo—ghosted or endlessly chatting with no real dates in sight.

This hasn’t been dating. It’s been clickbait.

AI is no magic fix. A bad system amplified by AI is just a worse system. But if the foundation is healthy, AI can serve as a supportive layer—an ally, not a manipulator.

Here’s how it can work:

  • Message to Meet: The only goal of messaging should be to decide whether to meet. No endless chats. No performative small talk.

  • One Like at a Time: You should only be able to express interest in one person at a time. No swipe sprees. No shopping-cart dating. Intentionality matters.

  • AI as Support, Not Substitute: Use AI where it can truly help: as a therapist to process dating anxiety, a coach to improve confidence and social skills, or a relationship assistant to help you reflect after a date. These are roles AI can play with empathy and discretion.

  • From First Date to Second: AI can prompt helpful reflections. "How did that go?" "Were you heard?" "Were you listening?" But this only works if the app is designed to nudge real human connection, not just more app usage.

Still, let’s not forget: friends do this better. They know you. They can set you up, give honest feedback, and remind you who you are. If you don’t have a few close human connections yet, maybe that’s where the journey should begin.

Because what we need is not more AI-generated romance—but less loneliness. And for that, the solution starts with redesigning the system to be human-first, AI-assisted, and love-centered.


A New Vision for Dating Apps: Start With the Self

A truly transformative dating app won’t begin by asking who you want to date. It will start by asking: who are you?

The first role of a great dating platform should be self-reflection. Using guided questions, therapeutic prompts, and maybe even AI-powered journaling, it helps you understand your values, desires, fears, and patterns. Before swiping on anyone else, you’re swiping through yourself.

Next, it helps you explore your expectations—not just listing them, but interrogating them. Are they rooted in reality or fantasy? Are they shaped by culture, trauma, ego, or hope? And as you better understand what you’re looking for, the app gently helps you temper expectations where needed. Not to lower your standards, but to root them in mutuality, growth, and human complexity.

And here’s the magic: because the app helps every user do this—know themselves, clarify their wants, and grow emotionally—it becomes a better matchmaker. You’re not just matched by proximity or photos, but by compatible journeys.

Then, it becomes a relationship coach. After the first date, it checks in. Were you heard? Did you feel safe? Are you aligned in values or just chemistry? As the connection grows, the app grows with you—offering nudges, guidance, and even tools for resolving early misunderstandings.

In this model, dating apps stop being marketplaces and start becoming mentors. They don’t just find you a date. They help you become someone who’s ready to build a healthy relationship—and match you with someone on the same path.

That’s not just a better app. That’s a better foundation for love.

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Emerging Monetization Models For Content Creators In The AI-First World

Emerging Monetization Models For LLM Platforms


A seismic shift underway: AI search and AI agents are eating the internet — or at least how it’s monetized. When AI gives people direct answers without clicks, the traditional ad revenue via traffic model collapses. So how can content creators adapt and thrive?

Here’s a detailed exploration of emerging monetization models for content creators in the AI-first world, beyond the blog-click economy:

🔁 1. Licensing Content to LLM Platforms (Content-as-a-Service)

Think: Getty Images, but for text and ideas.
As LLMs increasingly rely on high-quality data, they’ll need clean, curated, and reliable content sources.

  • Monetization Model: Syndication or licensing deals with LLMs, aggregators, and AI agents (e.g., “premium source feeds”).

  • Opportunity: Creators can form content collectives and negotiate licensing to LLM companies (OpenAI, Anthropic, Google, etc.).

🎯 2. Hyper-Personalized Content for High-Value Clients

Custom research, briefings, ghostwriting, or AI prompt libraries.

  • For consultants, authors, CEOs, thought leaders who need made-for-them content and are willing to pay.

  • Example: A political influencer pays $5,000/month for a daily AI brief built off your annotated research.

🧠 3. "Embedded Creator" Inside AI Agents

Imagine a popular creator’s “voice” or persona integrated into agents.

  • Monetize as a personality layer or knowledge flavor.

  • Example: A travel blogger becomes the default voice of a travel AI — and gets royalties per use.

  • This is like selling your “character” to a virtual agent platform.

📹 4. Video (and Audio) Thrives — But Needs Reinvention

Yes, video becomes even more defensible, especially when:

  • It’s deeply personal or human (vlogs, explainer videos with personality).

  • It integrates AI agents — e.g., interactive video content, AI-powered Q&A overlays.

  • Live + human = authentic = harder to clone.

  • YouTube channels might embed mini GPT agents trained on past content to keep fans engaged.

📬 5. Premium Community Subscriptions with AI Add-ons

Substack, Patreon, and Discord grow stronger, especially when combined with LLMs.

  • Give your community its own custom-trained GPT bot based on your writing, podcasts, or articles.

  • Add layers: Q&A, workshops, AMAs — powered by you and AI clones of you.

🛠 6. Productized Knowledge

Turn content into tools:

  • Notion templates, GPT prompt packs, calculators, dashboards, quizzes, micro-courses.

  • Example: A productivity blogger creates a $99 AI-powered daily planner and prompt set.

🧾 7. Interactive Content + Microtransactions

AI agents can deliver interactive, modular content experiences.

  • Think “choose your own adventure” stories or interactive essays.

  • Monetized by $1/$5 interactions, digital collectibles, or NFT-linked stories.

🎙 8. Voice Licensing + Audio Clones

If you have a popular podcast or distinctive voice, license a synthetic voice model of yourself.

  • Used in AI assistants, ads, or narration.

  • Royalty per use.

📚 9. AI-First Courses + Coaching

  • Offer GPT-enhanced courses where learners talk to an assistant trained on your method.

  • Combine it with one-on-one coaching or cohort-based workshops.

  • You don’t just sell knowledge—you sell the experience of interacting with you and your AI twin.

🧬 10. DNA of Your Work — Monetized via API or GPT Plugin

Turn your content into a structured knowledge base with an API or ChatGPT Plugin.

  • Users subscribe or pay per query.

  • Think: a climate science writer builds a GPT plugin to answer complex climate questions with their insights.

BONUS: Emerging Platforms & Tech to Watch

  • LangChain-powered knowledge bots

  • AI-native podcast platforms (e.g., where each episode has an LLM companion)

  • Custom GPT marketplaces

  • LLM-native blogging platforms (e.g., Netizen.page with built-in agent support)

📌 Conclusion

The new content monetization economy won’t be about pageviews or traffic — it will be about relationship, voice, trust, and tools. Creators who combine their unique perspective with LLM-enhanced delivery systems will win.






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Monday, June 02, 2025

Questions For Vinod Khosla

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