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Nick Saraev · YouTube

How to Use AI Like A Top 0.1% CEO

Nick Saraev breaks down seven concrete habit swaps that separate the highest-output AI users from everyone else: talk instead of type, keep AI listening through every meeting, show instead of tell, let AI interview you, demand flaws instead of opinions, and just try the thing yourself before assuming it's impossible.

Posted
yesterday
Duration
Format
Listicle
educational
Views
21.6K
695 likes
Big Idea

The argument in one line.

The gap between average AI users and the highest-output ones isn't a better model, it's a set of communication habits: voice over typing, always-on listening, visual outputs over text, and demands for criticism instead of approval.

Who This Is For

Read if. Skip if.

READ IF YOU ARE…
  • You use a chat-based AI tool daily for work and want more output from the same model, not a different one.
  • You're willing to try a voice dictation app, a background listening tool, or a visualization request without much setup friction.
  • You run meetings, write reports, or build decks and want your spoken words turned directly into finished work.
  • You've ever taken an AI's 'good idea!' response at face value and want a sharper way to get real feedback.
SKIP IF…
  • You're looking for a specific new AI model or tool recommendation; this is about communication habits, not software picks.
  • You already dictate to AI, run an always-on meeting assistant, and routinely ask for criticism instead of approval.
TL;DR

The full version, fast.

The highest-output AI users aren't using a better model, they're talking to it differently. People speak three to four times faster than they type, so dictating beats typing. An always-on voice stream turns meeting to-dos into drafted work automatically. Visualizing a request (3D, charts, mockups) beats describing it in text. Because models trained on human feedback are rewarded for agreeing with you, asking for opinions gets flattery, so ask what's wrong instead. Capabilities move fast enough that a month-old assumption about AI's limits is already stale, so the fix is to just try the thing yourself and stay open to new tool forms as they show up.

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Chapters

Where the time goes.

00:00 – 00:19

01 · Cold open — the real gap

Nick Saraev opens with the claim that top AI users aren't using a better model, they're talking to it differently, and previews seven upgrades drawn from making over $10 million with AI.

00:19 – 00:52

02 · Talk, don't type

People type at 30-50 words per minute but speak at 150-200, so dictating to AI gets more content in per minute than typing ever will; a free voice dictation tool, or a paid one called Hex, is the simplest setup.

00:52 – 02:16

03 · Set up an always-on voice stream

Top companies keep an AI listening through every meeting like an EA, turning spoken to-dos ('Ashley needs to do this, Sam needs to do this') directly into drafted work, sometimes finished before the meeting ends; the whole setup can run for a dollar or two a day.

02:16 – 03:18

04 · Show, don't tell

Rather than describing something in text, top users have AI generate a visualization with tools like Three.js, HTML, Unreal Engine, or Blender, such as a walkthrough of a new office with real lighting and Google Maps-sourced views, or an animated explainer of a report.

03:18 – 03:59

05 · Have AI interview you

Most people are bottlenecked by their own lack of knowledge and inability to fully verbalize what they want, so instead of stating a request outright, ask AI to interview you first about what it needs to know.

03:59 – 04:51

06 · Ask for flaws, not opinions

Because models trained with RLHF are rewarded for agreeing with you, asking 'what do you think' or 'is this email all right' defaults to flattery; asking 'what is wrong with my idea' or 'poke holes in my thinking' forces honest critique instead.

04:51 – 06:06

07 · Just ask it to do the thing

Instead of forming an opinion about whether AI can do something, open a frontier model and try it directly; capabilities and benchmark scores move fast enough that a month-old assumption about limits is already out of date, which is why operators like Jensen Huang keep these tools running on their own machines.

06:06 – 06:40

08 · Have an open mind

AI tools keep diversifying into new forms, avatars, wearable hardware, and the only way to know if a new form works for you is to try it; either you succeed or you fail, and both outcomes beat not trying.

06:40 – 07:00

09 · Close — Maker School

Nick closes by pointing viewers who want the business-use-case version toward Maker School, his 90-day accountability program with a money-back guarantee for landing a first customer.

Atomic Insights

Lines worth screenshotting.

  • People speak at 150-200 words per minute but type at only 30-50, so voice dictation gets more into an AI conversation than typing ever can.
  • An always-on voice stream during meetings can turn spoken to-dos into finished work before the meeting even ends.
  • A system that listens to a meeting and kicks off real tasks can run for a dollar or two a day, replacing an assistant who might cost $300-400 a day.
  • A picture is worth a thousand words and a video is worth 24 to 30 pictures a second, so showing AI a visualization beats describing one in text.
  • Asking AI to build a 3D walkthrough of a new office or home, pulled from real Google Maps imagery, beats staring at a flat floor plan.
  • Most people are bottlenecked not by what AI can do, but by their own ability to explain what they actually want.
  • Letting AI interview you with its own follow-up questions surfaces requirements you wouldn't have thought to state yourself.
  • AI models trained with reinforcement learning from human feedback are rewarded more for agreeing with you than for being right, so a direct opinion request defaults to flattery.
  • Asking 'what is wrong with my idea' instead of 'what do you think' forces an AI model past its default agreeableness.
  • Benchmark scores can climb double-digit percentage points within a single month, so a month-old opinion about what AI can't do is already stale.
  • The fastest way to find out if AI can do something is to open a frontier model and try it yourself, not to ask around.
  • Top operators actually have coding and AI tools open and running on their own computers instead of just hearing about them secondhand.
Takeaway

Seven habits separate top AI users from everyone else.

WHAT TO LEARN

The advantage isn't a better model, it's talking to AI by voice, keeping it listening constantly, showing instead of describing, demanding its questions and its criticism, and testing claims yourself instead of waiting to hear if something is possible.

02Talk, don't type
  • Speaking at 150-200 words per minute gets far more into an AI conversation than typing at 30-50 words per minute ever will.
  • A free voice dictation tool is enough to start; a paid option like Hex adds vocabulary training for better accuracy.
03Set up an always-on voice stream
  • An AI that listens through every meeting can turn spoken to-dos into drafted work before the meeting is even over.
  • A cheap always-on listening setup, roughly a dollar or two a day, can functionally replace a far more expensive human assistant.
04Show, don't tell
  • A picture replaces a thousand words and a video replaces 24-30 pictures a second, so asking AI to generate a visualization beats describing the same thing in text.
  • Tools like Three.js, HTML, Unreal Engine, or Blender let AI turn a flat plan, like a floor plan or a report, into something you can actually walk through or see.
05Have AI interview you
  • Most people are bottlenecked not by what AI can do but by their own ability to fully explain what they want.
  • Asking AI to interview you first, instead of stating a request outright, surfaces requirements you wouldn't have thought to mention.
06Ask for flaws, not opinions
  • Models trained with reinforcement learning from human feedback are rewarded for agreeing with you, so a direct opinion question defaults to flattery.
  • Asking 'what is wrong with my idea' or 'poke holes in my thinking' forces a genuinely critical answer instead of validation.
07Just ask it to do the thing
  • Benchmark scores can climb double-digit percentage points within a single month, so a month-old opinion about an AI's limits is already outdated.
  • The fastest way to know if AI can do something is to open a frontier model and try it yourself rather than asking around.
08Have an open mind
  • AI tools keep taking new forms, avatars, wearables, new interfaces, and the only way to know if one works for you is to try it.
  • Either trying succeeds or it fails, and both outcomes beat never finding out.
Glossary

Terms worth knowing.

RLHF (Reinforcement Learning from Human Feedback)
A training method where human raters score an AI's responses, which can teach the model to optimize for answers people approve of rather than answers that are strictly correct.
Sycophancy (in AI)
An AI model's tendency to agree with or flatter the user instead of giving an honest, critical answer, a known side effect of RLHF training.
Frontier model
The most capable AI model publicly available at a given moment, as opposed to older or smaller models.
EA (Executive Assistant)
A role, human or AI, that manages tasks, notes, and follow-ups on someone's behalf so they don't have to track everything manually.
Resources

Things they pointed at.

00:19linkStanford study — speech recognition three times faster than typing ↗
00:37toolHex (voice dictation tool) ↗
02:40toolThree.js ↗
04:00linkSharma et al. — Towards Understanding Sycophancy in Language Models ↗
04:00linkOpenAI — Sycophancy in GPT-4o ↗
05:37linkJensen Huang (CNBC) — using AI chatbots for first drafts ↗
06:40productMaker School
Quotables

Lines you could clip.

00:00
“The smartest people on earth do not use AI like you do.”
names the thesis in one line, no setup needed→ TikTok hook↗ Tweet quote
04:10
“You cannot just ask it, hey, what do you think about this?”
sets up the sycophancy problem in plain, provocative language→ IG reel cold open↗ Tweet quote
07:17
“The opportunity cost of not having done so is typically so goddamn high these days with tech being what it is, that it's just a logical decision.”
closing line with strong CTA energy for a clip ending→ newsletter pull-quote↗ Tweet quote
The Script

Word for word.

Read-along

Don't just watch it. Burn it in.

See every word as it's spoken — crank it to 2× and still catch all of it. The same dual-channel trick behind Amazon's Kindle + Audible.

metaphor
The smartest people on earth do not use AI like you do. And the difference is not a better model or some sort of fundamentally different tool. It is almost entirely in how they talk to it.
So I've made over $10 million over the course of the last few years with AI and AI tools. And I want to show you seven simple upgrades you can make to the way that you work with AI to make significantly more and significantly less time. The first is talk, don't type.
The average person writes at approximately 30 to 50 words per minute with a keyboard, but they speak at 150 to 200 words per minute. Not every one of those words is going to be perfect, but when you do the math, you end up being able to get more out onto AI than you can if you just use your fingers. Don't let your fingers be the bottleneck.
Your voice box is developed for far more than that. The simplest way to get the setup is use a free voice dictation tool. You don't need to pay any money to anybody.
Just type like free voice dictation tool on Google if you don't believe me. Otherwise, use a tool called Hex. I've used it myself.
You also have the ability to like train it on a bunch of your particular words and vocabulary to make it a lot better. The next is set up some sort of always on voice stream during all of your recordings and meetings. So the world's best companies take advantage of voice dictation software, things like GPT RealTime 2 .1, to basically always have an assistant listening to everything they do.
and say during a meeting or some other sort of social activity. You can think of this as an EA that is always with you taking meeting notes and then turning that into some sort of actionables or project file. The craziest version of this is I built a couple of systems that will take what you say during the meeting and actually go and build the thing that you said you needed to build.
So, you know, how many to -dos do you generate during a meeting? Oh, Ashley needs to do this. Sam needs to do this.
Sarah needs to do this. You guys can actually go and like do the task. during the meeting so you have the results back while you are still in said meeting.
I'm not a fan of meetings, but this makes them like 100x more productive. The simplest way to set this up is use a frontier LLM that is the best LLM that you can and say, hey, I want to build something that essentially listens to my mic and maybe screenshots my page. and lists the system audio so that it can hear everything that goes on during a meeting.
And then I want you to be able to transcribe the whole thing and then use it to actually kick off real projects in the systems that I use day to day, whether it's like Notion or Asana or Monday or one of the many ones. This sounds really crazy, but you can actually build the system and have it running extraordinarily cheaply for like a dollar or two a day.
And then this dollar or two a day system can legitimately replace a hire that might cost you, you know, 300 or 400 bucks a day if you wanted like a really competent EA. We're actually there right now with that technology. Believe me.
The next is to visualize things using 3JS, HTML, or some sort of visual render like Unreal Engine, rather than tell the best people always show. So they don't spend a bunch of time in word tokens, okay? A picture is worth a thousand words, and then a video is worth 24 to 30 pictures a second.
So that's what they do. They'll create visualizations, usually using tools like 3 .js, HTML, Unreal Engine. blender and so on and so forth.
And they actually have the agents do it for them to explain things better. So an example is maybe your company is getting a new office or maybe you are moving into a new home. Don't just look at the floor plan.
Actually, like tell AI, hey, create me a 3D visualization of this with realistic lighting conditions, pulling images off of Google Maps and stuff like that to look out the windows and then plot me in it so I can walk around and see what it looks like. Then I want you to add real furniture, let's say, with Amazon checkout links such that if I like a product, then I can actually just click a button and then buy it.
You could have it visualize dense, complicated reports. You could have it visualize analytics that occur on a daily or weekly basis. You could turn it into an animated explainer, just like the one that you guys are currently watching.
Basically, just use AI to show, not tell. Next is to have AI interview you. So all top AI users are doing some version of this right now.
We've had a variety of different skills that have come out of this, like grill me, interview me, roast me, and so on. But basically, if you want to get an AI to do what you want to do, it's not enough just to tell it what you want it to do. A lot of the time, you yourself are bottlenecked by a lack of knowledge and also a lack of explicit verbalization ability of the thing that you want.
And so better than being, again, bottlenecked, piping the ocean through a straw of your experience, just have AI ask you, hey, I want to do X, Y, and Z. Could you just ask me anything and everything that I might need to know in order to give you the best way of doing the task? If you feed this to one of the frontier models, it will do an extraordinary job, no problem.
The next is do not ask for opinions. Ask for flaws. AI models are trained using reinforcement learning through human feedback or RLHF.
That means that oftentimes they are rewarded more for agreeing with you than actually giving you the right answer. So in consumer applications, they'll typically default to agreeing with you even if your answer is wrong. That means you cannot just ask it, hey, what do you think about this?
Hey, is this email all right? Hey, what do you think about my project? If you do that, it will always default to saying something good.
And maybe that's not going to be the case in a couple of years from now, but it definitely is right now. So instead of asking for generalized opinions, always ask for what is wrong with some deliverable or output. Always say, you know, what is wrong with my idea?
Poke holes in my thoughts, you know, roast my essay. The whole idea is like it needs to hear from you that you are explicitly asking it to give you bad feedback. And then with that bad feedback in mind, you yourself can be the judge of whether the bad outweighs the good.
Next, if you're skeptical about whether AI can do something, literally just ask it to do the thing for you. Do not rely on opinions from people like me or people on the internet in the time that it takes to even finish this video. you probably could have gotten a frontier model to do a lot of the things that you were wondering, hey, you know, can I do this with a frontier model?
And I'm not saying this because I don't want you to watch my YouTube videos, obviously, but because I want you to be very familiar with and understand that things are changing so quickly nowadays. They're accelerating so fast that what I say today may not be necessarily true tomorrow. Even if it's the same model, there might be a new way of doing things, some sort of harness update under the hood.
You know, literally every day that passes, capabilities updates and then benchmark scores are going up by like percentage points such that, you know, at the end of the month, performance on a benchmark that previously might have been considered impossible might now be 50 % or more. So this is what top CEOs do.
They actually go and they actually try the thing themselves. You know, Jensen Wong, for instance, he's not just like listening to people talk to him all day. He actually has cloud code, codecs, NVIDIA's, whatever the hell models he's running, set up on his computer and he is typing things in and he is voice transcribing things in.
You know, like big CEOs actually have these tools set up on their computers and they are trying these things constantly because they understand that first principles is typically the best way to answer the question, is this thing possible? Best way to answer the question, is this thing possible, is by going out and doing it at least once.
Finally, have an open mind. These AI tools are going to diversify into many forms. You'll probably have like AI avatars on your screen pretty soon that are chatting with you just like a coworker might.
You also might have totally different ways of doing things. You might have different sorts of hardware, little pendants that you wear and stuff like that. Try them out.
right just like the previous one you're not going to know until you know and the only way to know is to try either you're going to try succeed or you're going to fail and that's totally okay either way you will have explored that opportunity. And the opportunity cost of not having done so is typically so goddamn high these days with tech being what it is, that it's just a logical decision.
Hopefully you guys appreciated this video. If you guys want to learn more about how to use AI like a business person, somebody that achieves a lot, definitely check out Makerschool. It's the first link in the description.
It's my 90 -day accountability program where I will help you get your very first customer for an AI or automation service. If I don't, I give you all of your money back. Aside from that, please like, comment, and subscribe, and I'll catch all y 'all in the next video.
Thanks.
The Hook

The bait, then the rug-pull.

Nick Saraev opens with a direct claim: the gap between average AI users and the people making millions with it isn't a better model, it's how they talk to it. What follows is seven concrete habit changes, not tool recommendations.

Frameworks

Named ideas worth stealing.

00:19list

Seven Simple Upgrades

  1. Talk, don't type
  2. Set up an always-on voice stream
  3. Show, don't tell
  4. Have AI interview you
  5. Ask for flaws, not opinions
  6. Just ask it to do the thing
  7. Have an open mind

The full checklist structuring the video, tracked on-screen as a sidebar that fills in green as each item is covered.

Steal fora personal AI-usage audit or an onboarding checklist for a team adopting AI tools
03:59concept

RLHF-driven sycophancy

Models trained with reinforcement learning from human feedback are rewarded more for agreeing with the user than for being correct, so direct opinion requests default to flattery.

Steal forframing why a direct AI opinion can't be trusted at face value, and reframing feedback prompts to force criticism
CTA Breakdown

How they asked for the click.

VERBAL ASK
06:40product
“If you guys want to learn more about how to use AI like a business person... definitely check out Makerschool. It's the first link in the description. It's my 90-day accountability program where I will help you get your very first customer for an AI or automation service. If I don't, I give you all of your money back.”

single plug in the final 20 seconds, framed around a money-back guarantee rather than urgency

MENTIONED ON CAMERA
00:19linkStanford study — speech recognition three times faster than typing ↗
00:37toolHex (voice dictation tool) ↗
02:40toolThree.js ↗
04:00linkSharma et al. — Towards Understanding Sycophancy in Language Models ↗
04:00linkOpenAI — Sycophancy in GPT-4o ↗
05:37linkJensen Huang (CNBC) — using AI chatbots for first drafts ↗
FROM THE DESCRIPTION
PRIMARY CTAWhere the creator wants you to go next.
OTHER LINKSAlso linked in the description.
Storyboard

Visual structure at a glance.

open
hookopen00:00
voice dictation
valuevoice dictation00:38
EA always with you
valueEA always with you01:10
3D visualization mockup
value3D visualization mockup02:55
bottlenecked
valuebottlenecked03:37
hard benchmark chart
valuehard benchmark chart05:28
CTA
ctaCTA06:40
Frame Gallery

Visual moments.

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