How to outsource everything to AI & get dumb:

(a brain rot guide)

If you outsource everything to AI, you will get very dumb.

This is how most people (you?) outsource to AI:

You think AI will magically understand what you meant. It won’t.

Just like you outsourced your orientation to Google Maps, and you got dumb.

I am so bad at orientation myself.

Like, I am from Paris. I have lived most of my adult life there. But I can’t walk around without a GPS. I can’t help it: I put my destination, I follow the little blue dot, and I don’t even look up anymore.

where is the Eiffel tower??? better check my GPS

It’s actually so bad I believe if you teleport me to Paris in the 80’s, without a phone, I’d be as lost as a tourist.

That was me & orientation. I could also mention…

1. Phone contacts → I know mine, and my mom’s. That’s it.

2. Spelling mistakes → Writing without an autocorrector isn’t fun, at all.

3. Mental math → Quick maths: 180 x 45? Yep. You’re opening your calculator.

4. Attention span → I remember how slow 3G internet was. If I were given 3G today instead of my regular 5G, I would go crazy. That’s ridiculous.

5. Photography → I expect everything to be on my phone. It’s stored, forever. So I stopped checking my old photos. There are too many. Lost the charm.

6. Calendar → I remember my family birthdays, a couple of friends. But if anything isn’t on my calendar, my brain is now really good at skipping it.

7. Music → If it’s not on Spotify or recommended by the algorithm, I don’t dig for novel songs anymore. But that used to be my job as a label owner.

You know where I am going with this. Today, there is AI.

Let’s say you face a new problem. You open AI. You can outsource your thinking to AI. But you can’t outsource your understanding.

from a guy I like on X - spot on, but now how do we avoid it?

You don’t want AI to take over the same way Google Maps made me orientation-dumb. And you’ve been there before, the AI “making you dumb”.

• You fix a slide deck because the AI version was off.

• This client’s email you rewrote 3 times because Claude’s draft sounded like a stranger. You imagine the client calling you out for using AI… you can’t.

• The spreadsheets you quickly built for your team, but you now have to cross-check every formula on every tab. Manually.

You’re using AI because it saves time → and you end up wasting a lot of it.

But there’s a way to use AI without outsourcing your brain. It costs you a couple of extra minutes of thinking on the front. It saves you hours on the back. And the work that comes out the other side is yours, not Claude’s.

I will share my own real-life scenario, 5 steps, as a detailed use case. Then, 3 more examples at the end of this newsletter, so you can see the same method run across very different work.

This is the guide I wish someone gave me before I wasted months trying to outsource everything to AI.

1. Save this guide and spend 30 minutes this weekend to ask AI better.

2. Send it to anyone asking you, “AI makes me dumber, how can I do it better?”.

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I - How I outsource work (not thinking) to AI.

Last month, I got on a video call with the Chief of Staff.

240-person company. And 25 minutes in, she said: “I need to roll out Claude across the whole org. Help me sell you internally.”

By Friday, she wanted 3 things from me, all written in her voice, that she could forward to her CEO and exec team to get the rollout approved:

1. A 90-day Claude adoption roadmap.

2. A 1-page pricing breakdown (her side, my side, Claude side).

3. An email she could send to her exec team to kick it off.

Not to mention her credibility partially on the line for the recommendation.

The default version of me opens Claude and types: “Write a 90-day AI adoption roadmap for a 240-person company.” Maybe write a couple of lines about the company as “context” to feel good. Hits send. Copies the answer into a Word doc.

And it’s boring. Generic. Her CEO would smell it. Should I start again?

But here’s what the new version of me does:

Step 1. Give Claude more context than you think.

We must answer 4 questions before doing the work.

1. Who’s the audience?

2. What’s the one thing they should walk away with?

3. What format am I copying? (Paste a real example. Not “don’t sound like…”)

4. What are the stakes?

For my example:

1. Audience. 3 layers.

   • The Chief of Staff. 4 years in role, came from PE. Hates being sold to. Minute 12 of the call: “If this rollout fails, my CEO wants to contact Anthropic’s team.” Everything I send her has to land as risk reduction.

   • Her CEO. Reads the first paragraph and the dollar figure. That’s it (the Chief of Staff said so on the call). Phase 1 of the roadmap and the pricing one-pager have to land in 90 seconds of skim.

   • Her 8-person exec team. 2 former MBB consultants (they’ll dissect the methodology). 3 operators (allergic to slideware). 3 technical (will ask about SOC 2 and data residency on slide 1). I write the email for the most skeptical of the 8: the CTO who shipped 2 failed AI pilots in 2024.

2. Walkaway.

   • From the CEO: a 1-line Slack to the Chief of Staff that says “approved, send me the contract.”

   • From the CTO: a private DM to the Chief of Staff: “this one’s different. I’m in.”

   • It sounds cliché, but Claude is extremely good at understanding goals.

3. Format.

   • Roadmap = the 90-day plan I built for a similar engagement that closed 6 weeks ago. Phase 1 has to be 1 page. Phase 2-3 can be denser.

   • Pricing one-pager = her finance team’s vendor procurement template (she dropped it in our chat during the call).

   • Email = 180 words max. No bullets. 2-word greeting max. (Her last 5 internal emails were all under 200 words. No warm-up.)

4. Stakes. The Chief of Staff mentioned a potential co-hosting event, to invite their clients. And most of their clients are potential clients for us.

That’s about 12 minutes of thinking.

Some answers came from memory. Most from a Claude chat with connectors to my notetaker, my email, and my team’s channel. We must capture everything.

But where? Claude has so many different faces:

• Claude chat (browser).

• Claude chat (app).

• Claude chat Projects (app).

• Claude Cowork (app).

• Claude Cowork Projects (app).

• Claude Code (app).

Let me walk you through the best way to do it.

#1 : Go to Cowork → Projects → New project.

#2 : Start from scratch.

Now we need to extract my meeting notes, my emails & the Slack thread conversations I had about this specific client.

#3: I make sure my Connectors are activated inside Claude.

Here’s my prompt:

Use my Granola, Gmail and Slack Claude Connectors.

Extract all of the data from my call and emails threads and conversation with [my partners] regarding [client].

I want to answer:

1. What's the need?
2. What's the audience?
3. What's at stake?
4. What's the format they want?
5. What's the blocker?
6. What's the walkaway we want?

And it looks like this:

#4: Claude went through my meeting notes, email, and team’s channels. I had to blur info, though. But I’ll continue the example.

#5: And Claude gave me most of what I needed.

Let’s keep prompting in the same Claude Projects.

We can now start explaining what we need from Claude.

But we’re still in research mode:

I want [deliverables] for [stakeholder].

But I need to run a deeper analysis to catch what our conversations and meetings didn't catch. For example: 

1. I need [company] last annual report and 3 recent press releases. Is it still relevant? Does it help us?
2. What else could you find from deep searching the web on [stakeholder] and [company]?

Make sure you turn on Claude Research before sending the prompt:

#6: It’s definitely an underused tool.

#7: Claude will ask you clarifying questions. Simply answer.

115 sources online. And it searches, knowing our context already.

9 pages of deep research. Unmatched.

That’s it for the prep work.

And you’ve now done more prep work than 99% of the people opening Claude. And you still haven’t asked for a single sentence of the draft.

Step 2. Ask for the thing.

This is actually the simplest step.

Here I have 3 deliverables. So I will ask 3 times:

Now that you have all of the necessary context, I need to send to [stakeholder] a [deliverable] for [success criteria] in [format].

I prefer asking 3 times (so 3 different prompts) instead of 3 asks in one prompt.

You simply get more reasoning, and a better answer every single time.

Because we have so much context, Claude is filling out tons of steps before answering. This is how you get smart AI, not the average.

That’s one way. I can now change the design at will. Like, McKinsey style? Let’s do it.

I can’t show. You just have to trust me aha.

PS: If you didn’t turn on Opus 4.7 + Adaptive Thinking, it’s time to do it:

Bottom right, select the right model. It’s the smartest.

Step 3: Treat the first answer like a draft from a junior intern.

Claude’s first answer is rarely the right answer. Useful as a draft. Dangerous as a deliverable. Don’t accept it. Push back before her CEO does.

3 prompts I love to use.

Move 1. Roast it. Paste the output back in:

What's weak about this roadmap? Where would the CEO push back?
Give me 5 honest critiques as if you were the CEO trying to reject this.

Then make Claude fix what Claude just wrote.

Move 2. Reverse the brief. Ask Claude:

What did I forget to tell you that would have made this stronger?

This is where I find out I didn’t include her industry’s compliance constraints. Step 2 didn’t quite hold. Back to the folder. Add the SOC 2 documents. Restart.

Move 3. Compare to gold. Paste in the roadmap I built for the client that closed last quarter:

Why is your roadmap worse than this one? Rewrite to match the structure and confidence.

For this engagement, I run all 3 moves on the roadmap. Move 1 on the pricing one-pager. All 3 on the email.

Step 4. Follow up much more than you think you should.

1-turn AI chats won’t get you far.

The good stuff is in the follow-ups. Turn 12. Turn 27. Turn 41.

Two tricks make this fast/cheap enough.

Wispr Flow. It’s a dictation tool. You talk, Wispr Flow types.

I stopped typing follow-ups 2 years ago. I talk to Claude. 200 words per minute instead of 60. Each follow-up costs less effort. So I follow up much more.

I wrote a newsletter on how to set up Wispr Flow.

AskUserQuestion (inside Cowork). Ask Claude to use AskUserQuestion to ask you questions. It’s my favorite Claude feature. Here’s an example:

Claude creates a form and you just click on what you prefer.

Instead of me prompting Claude, Claude asks me 3-4 questions and builds from my clicks.

For this example, I run roughly 40 follow-ups across the 3 deliverables.

The non-obvious ones:

• “Argue against this roadmap as the CFO who hates new vendor spend.” I take the strongest counter and bake it into Phase 1.

• “What would the IT director secretly fear about this rollout?” I quietly remove the part she’d fear.

• “Rewrite this email line by line as if she wrote it. Not me.” This one alone takes 10 follow-ups.

• “If this rollout fails by month 3, what’s the most likely reason? Now bake the prevention into the roadmap.”

If you finished an AI task in 2 turns, you stopped too early.

Step 5. Read the whole thing one more time before you ship.

Even after 47 follow-ups, the final output has tells. AI rhythm. A banned word. A bullet that doesn’t earn its place. A claim you can’t back up.

So this is what I did:

• I read the email out loud, twice.

• I read the pricing one-pager as her CFO: are the numbers defensible? Are the assumptions clear?

• About 25% of the original draft gets cut.

I usually delete stuff instead of writing new ones. AI writes too much, details too much. And I prefer that (so I can clean instead of adding new things).

On Friday morning, I send the Chief of Staff the 3 needed documents. About 2 hours of total focused work across a few days.

Now ask yourself the obvious question:

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II - “If it’s that long, why even use Claude?”

Because you can’t hold 30,000 words of client context in your head.

Claude can.

You can’t run 3 versions of a roadmap through 3 different stakeholder lenses in parallel.

Claude can.

Set up your Cowork folder once and the next engagement for the same client is half the work. Use Wispr Flow and AskUserQuestion right, and 40 follow-ups feel like 5. Save your folders and prompts as templates, and the third engagement of the year takes 20% of the time of the first.

Claude takes each of the 5 steps from 10 hours down to 20 minutes.

Up to you to use the free time to do better work now.

AI was never meant to be lazier, or faster. But making you more capable.

And most people are not more capable with AI.

This is how most people outsource to AI:

They think AI will magically understand what they meant. You need to be more precise. Steer it better.

I shared one example of how to use AI properly. Let me share three more:

This newsletter grows from your shares. I want to help a million people use AI right. Sharing keeps your articles free & helps me stay laser focused on mastering AI for you.

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III - 3 more examples of outsourcing to AI.

You’ve seen the method on a heavy consulting engagement. Here’s the same 5 steps run across 3 very different jobs. But I won’t explain so much this time.

Use case 1. LinkedIn post for a life coach.

The setup. A life coach wants to teach corporate teams to think like elite athletes. She needs 1 LinkedIn post a week. And she needs to use AI correctly.

Step 1. Audience = burnt-out middle managers in tech. Walkaway = 1 athlete habit they can copy tomorrow morning. Format = her viral post from June (paste it). Stakes = newsletter signups.

Step 2. Context dropped into Claude: her last 30 LinkedIn posts (so it learns her voice) from her Google Drive connected to Claude, 3 long-form interviews with the athlete she’s referencing (so the quotes are real, not hallucinated), the corporate burnout stat she always cites, and her tone.

Step 3. Roast move only. 1 paste-back: “Rewrite this with the punch of her best post.” Done in 2 minutes.

Step 4. 10 follow-ups. Mostly cutting and sharpening the hook. Wispr Flow makes it 4 minutes.

Step 5. She reads the post aloud. If she stumbles on a line, we rewrite the line. 30 minutes total from idea to scheduled.

Ship: 1 post in her voice, scheduled for 9am Monday. It looks like this:

That’s a first draft I would definitely edit.

After a couple of follow-up, the post looks better.

Use case 2. A 30-page market report into a deck.

A logistics partner asked me whether they should expand into the peptide market. Deliverable: a 30-page report and a board-ready deck.

Step 1. Audience = their board (8 people, 6 of whom are 60+). Walkaway = a clear yes/no with the 3 strongest reasons either way. Format = a sample McKinsey market-entry report (paste it). Stakes = the engagement plus a retainer if they say yes.

Step 2. Context: their last 2 annual reports, the FDA’s recent peptide guidance, 4 peptide-market analyst reports, financials and ops summaries on 6 competing logistics players, transcripts from 3 peptide companies they could service. About 180,000 words into a Cowork Project.

Step 3. I like to sometimes roast Claude by using ChatGPT. I will go and use ChatGPT’s Deep Research (its own Research) mode and add it to my Claude Cowork Project. I will then ask, “What did you forget that ChatGPT didn’t miss?”

ChatGPT asks you questions before starting the Deep Research. It sometimes takes hours (!!!) to complete.

The result is a giant research document you can upload to Claude.

Always choose export to markdown, and then put it inside Claude.

Step 4. Dozens of follow-ups across multiple Claude chats (inside the same Project). Each market segment gets its own thread.

Step 5. I read the full report in 1 sitting on paper. Spot-check 10 random data points (do the sources hold?).

Once done, I make sure my Gamma connector (to make slides) is active.

And I simply prompt “Use the Gamma connector to make the necessary slide”.

Claude uses Gamma’s connector to make the slides.

And I can go on Gamma to edit it better.

Ship: 30-page report and 22-slide Gamma deck.

Use case 3. Spreadsheet sent to a CTO.

A CTO asked me whether her 50-person team should invest in building their own open-source LLM stack (Llama or Qwen on H200 GPUs) or just keep paying Anthropic for Claude API. He needs a financial model showing token economics over 6 months.

Step 1. Audience = his CFO and the board. Walkaway = the break-even point in months, and the answer. Format = a 4-tab Excel model with sensitivity analysis (paste a model that closed a board vote last year). Stakes = $3M+ in CAPEX or 6 years of API spend, depending on the call.

Step 2. Context: current Claude API tiered pricing, open-source model benchmarks (Llama, DeepSeek, Qwen), H100/H200 GPU CAPEX vs. AWS rental rates, salaries for the 3-person ML infra team they’d need to hire, 12 months of her team’s actual token consumption logs.

Step 3. Move 1 only. “What did I forget in this model? Where would my CFO call BS on the assumptions?” Claude flags 3 missing line items (fine-tuning costs, model retraining, evaluation infra). I add them.

Step 4. 10 follow-ups. Most of them iterate on scenarios: base case, best case, worst case. Plus 3 stress tests. What if Claude pricing drops 50%? What if open-source quality matches Claude next year? What if GPU prices double?

Step 5. I rebuild the spreadsheet manually for 1 month of usage.

Ship: 4-tab Excel model and a 1-page executive summary.

The Excel file is complete, 4 tabs after extensive research.

And I simply asked for a 1-page summary as a follow-up.

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