Scripts & Templates

Hey,

Welcome. I am glad you are here.

You signed up because something about "AI without the hype" resonated. Maybe you are tired of the breathless AI predictions. Maybe you are trying to figure out where AI fits in your work and the advice you are finding is either too technical or too vague. Maybe you just want a straight answer from someone who actually builds this stuff.

Whatever brought you here, let me tell you what you are going to get:

Every week, one email. Inside:
- Practical frameworks for making AI and automation decisions
- Honest takes on what works, what does not, and what is overhyped
- Lessons from building production AI at Meta and teaching GenAI at IE University
- Real builds: what I am building, how, and what I learned

No jargon walls. No "10 AI tools that will change your life." No fear-mongering.

Quick intro: I am Doron Vainrub. I build data platforms and automation systems at Meta -- the production infrastructure that keeps AI running at scale. Outside Meta, I teach GenAI to executives and MBA students at IE University in Madrid. I have taught 200+ students across audiences from tech bootcamps to boardrooms.

That combination is where this newsletter comes from. I see what actually works in production AND what confuses smart people about this technology.

Tomorrow, I will send you the framework that anchors everything I write about. It is called the 4D AI Adoption Filter, and it is the single most useful tool I have built for evaluating AI initiatives.

Talk soon,
Doron

P.S. If you have a burning AI or automation question, reply to this email. I read every reply and your questions shape what I write about.

Hey,

Yesterday I promised you the framework that anchors everything I write about. Here it is.

I call it the 4D AI Adoption Filter. It is four questions, asked in order, before you commit resources to any AI initiative. Skip one and the rest collapse.

Gate 1: DATA -- "Do we have the data?"
No data, no AI. This sounds obvious, but I have watched organizations spend months scoping AI projects only to discover their data is scattered across spreadsheets, legacy systems, and someone's email inbox. Before anything else: audit your data.

Gate 2: DEMAND -- "Does it solve a real problem?"
"We should use AI" is not a problem statement. What is the specific pain? Who feels it? How painful is it? If the problem is not painful enough, the solution will not stick.

Gate 3: DELIVERY -- "Can we deploy AND maintain it?"
Shipping v1 is 20% of the work. The other 80% is keeping it running. Who maintains it? What happens when the model drifts? Do you have the operational maturity to support this six months from now?

Gate 4: DEFENSIBILITY -- "Is this durable or a gimmick?"
If a competitor can replicate your AI feature in a week with the same API, it is not a moat. Is this a genuine advantage or just a head start?

Most AI projects fail because they start at Gate 3 (let's build something!) without passing through Gates 1 and 2.

Try this: pick one AI initiative your team is considering. Write down the answers to these four questions. Be honest. If the answer to "Do we have the data?" is "sort of," that is a red flag worth investigating before anything else moves forward.

I go deeper into each dimension -- with diagnostics, scenarios, and common mistakes -- in my newsletter issues. But this framework alone will save you from the most expensive AI mistakes.

Tomorrow, I will send you one last email: my best resources for getting started.

Talk soon,
Doron

Hey,

This is the last onboarding email. After this, you will get the regular weekly newsletter every Wednesday.

Here are the best resources to get started:

1. The 4D Filter Decision Tree (free PDF)
A 1-page visual tool that walks you through the four gates. Print it out and bring it to your next AI meeting. Reply "framework" and I will send it.

2. "AI Theatre vs AI Plumbing"
My most-shared piece. How to tell whether an AI initiative is real substance or an impressive demo that will die in six months. Look for it in an upcoming newsletter.

3. "The 3 questions every founder should ask before investing in AI"
Before you spend a single euro on AI, these three questions will save you from the most common mistakes. This is where I recommend most leaders start.

4. "The 3 principles behind every automation that actually works"
From building automation frameworks used across hundreds of production workflows: the three principles that separate automation that lasts from automation that breaks.

One request: if you find value in this newsletter, share it with one person who is trying to figure out AI for their team or business. The best growth for this kind of content is word of mouth from people who trust each other.

That is it. You are all set. From here, expect one email per week with practical, honest AI guidance.

Most leaders make AI decisions based on hype or fear. You chose a different path.

Welcome aboard.

Doron

P.S. Hit reply anytime. I read every response and your questions directly shape future issues.