Latest / AI Fire Daily / #695 Neil: Which of These 9 AI Business Ideas Is Worth Your Time?

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#695 Neil: Which of These 9 AI Business Ideas Is Worth Your Time? · AI Fire Daily

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  Right now, at this exact moment, someone with
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  absolutely zero coding experience is just sitting
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  on their couch. Yeah, literally zero. Right,
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  zero experience. And they're typing plain English
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  into an AI prompt and building a fully functional
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  software company in, what, maybe three hours?
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  Oh, easily. Sometimes less. But the crazy thing
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  is, statistically speaking, almost every single
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  person who attempts this is going to go totally
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  bankrupt. It is, uh, it's the ultimate modern
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  paradox, really. Yeah. What's fascinating here
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  is that we have finally achieved this massive
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  democratization of technology, you know. But
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  when the barrier to entry drops to absolute zero...
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  The value drops right along with it. Exactly.
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  The financial value of the thing you are building
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  just plummets. Okay, let's unpack this. Because
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  we're looking at a landscape for 2026 that has
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  completely inverted all the old rules of business.
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  It really has. I mean, it used to be... that
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  if you understood the complex tech, like early
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  spreadsheet software or the foundational internet,
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  you were basically indispensable. Right. Your
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  knowledge of the tool was your economic moat.
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  Exactly. But now the tools are effortless. So
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  for everyone listening to this deep dive, the
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  huge question is, how do you actually make money
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  when everyone has the exact same superpower?
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  That is the trillion dollar question. It is.
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  So today we are tearing into a massive stack
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  of research that ranks nine distinct AI business
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  ideas for 2026. And we're analyzing them through
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  a pretty strict tier system, right? Yeah, we
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  are. We're starting at tier C, which represents
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  businesses with just massive competition and
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  super low barriers to entry. The easy stuff.
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  Right. And then we're working our way all the
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  way up to tier S, which is the strategic peak
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  of long -term growth and, you know, true defensibility.
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  But we're not just going to list these off like
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  a textbook. We have to dissect the actual underlying
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  mechanics. Like, why do some of these scale into
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  million -dollar enterprises while others just
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  completely collapse the moment a new AI model
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  drops? Because they will collapse. Because they
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  will. If you don't understand the why, you're
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  just gambling. So true. So if you're trying to
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  figure out where you fit into this new economy,
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  whether you're looking for a side hustle or pivoting
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  your career or just trying to protect your current
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  job, We're going to map out the exact mechanics
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  of what works. Let's jump right into the bottom
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  of the pyramid. Let's do it. Tier C. Tier C.
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  The trap of the quote unquote easy business.
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  First up is AI software as a service or SaaS.
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  The appeal of AI SaaS is just incredibly seductive.
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  Oh, absolutely. You build a digital product once
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  and you sell it an infinite number of times with
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  essentially zero marginal cost. And the barrier
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  is practically gone now. Using coding agents
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  like Cloud Code or visual builders like Lovable,
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  you just don't need a dev team anymore. You really
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  don't. There's actually a case in our research
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  about a raw AI SaaS framework that hit $1 million
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  in annual recurring revenue. The creator basically
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  used Cloud Code to iterate at a speed that left
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  traditional manual developers completely in the
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  dust. Right. The quote from the source was that
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  slow devs just lose out. But, and this is a big
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  but, We have to look at how that actually operates
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  under the hood. OK, let's get into the weeds.
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  A lot of these early AI SaaS products were what
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  the industry calls wrappers. Right. I've heard
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  that term thrown around a lot. Explain the mechanics
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  of a wrapper for a second, just so we're all
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  on the same page. Sure. A wrapper is essentially
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  a very thin layer of user interface sitting directly
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  on top of an existing foundational model, like
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  OpenAI's GPT -4 or Anthropix Claude. So you aren't
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  building a new AI at all. Exactly. You are just
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  writing a specific hidden prompt. Say, uh, act
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  as a professional resume writer and putting a
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  nice web design over it. Which brings up a massive
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  red flag. Huge red flag. If my entire startup
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  cost is just a $50 monthly subscription to a
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  tool like Lovable, and my backend is literally
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  just OpenAI's API, what stops someone else from
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  doing the exact same thing tomorrow? Nothing.
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  Absolutely nothing. I mean, it feels like opening
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  a coffee shop right next to 100 identical coffee
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  shops. If the startup cost is just a paid plan,
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  isn't customer retention basically impossible?
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  It's worse than that, actually. It's like opening
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  a coffee shop where your supplier regularly updates
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  their coffee machine and in the process accidentally
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  unplugs yours. Oh, wow. That's platform risk.
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  If you build a wrapper for, say, PDF summaries.
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  And then Apple or Google just builds native PDF
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  summaries into their next operating system update.
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  Your entire business just evaporates overnight.
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  Overnight. Poof. Gone. So is the AI SaaS model
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  just dead on arrival for a solo entrepreneur?
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  Like, should people just avoid it? No, not necessarily.
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  But you have to completely invert the traditional
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  Silicon Valley model of, you know, build it and
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  they will come. Right. The most critical mechanism
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  for success in tier C is validation through friction.
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  before you ever write a line of code or prompt
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  an agent to build software, you have to sell
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  the result as a manual service. Like doing the
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  heavy lifting yourself behind the scenes. Exactly.
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  Find a specific group of people with a hyper
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  -specific problem. Let's say real estate appraisers
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  who spend four hours a day formatting localized
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  housing data into a specific state compliance
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  report. Okay, super niche. Very niche. You offer
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  to do it for them for a fee. You manually use
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  AI tools to generate those reports. Once you
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  understand the messy, nuanced reality of their
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  workflow. And you prove they'll actually pull
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  out their credit card and pay cash for it. Yes.
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  Once you prove that, then you use cloud code
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  to automate that specific workflow into a SaaS.
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  You are building defensive software around a
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  validated human need, not just wrapping a neat
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  AI trick. That makes total sense. You're monetizing
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  the workflow, not the underlying AI model. which
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  pushes us to the second idea in TRC, which is
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  AI website building. Right. The tools in this
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  space, like Loveable, Claude Code, Codex, the
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  research compares these agents a lot for speed,
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  design, and prompt accuracy. And you can literally
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  prompt an entire multi -page website into existence
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  for a local business in maybe two hours. It is
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  the ultimate beginner's entry point, really.
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  The strategy usually looks like this. You generate
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  a gorgeous sample site, walk into a local bakery,
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  and tell the owner you'll give them this new
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  digital storefront for a few hundred bucks, plus
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  a monthly hosting fee. But the flaw is staring
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  us right in the face. As these visual builders
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  get cheaper and more consumer friendly, the perceived
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  value of basic HTML is plummeting to zero. The
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  bakery owner is going to realize they can just
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  type, make me a bakery website, into an AI themselves.
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  Why on earth are they paying you? They won't.
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  I mean, if all you are selling is a digital brochure,
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  they absolutely won't. To escape the gravity
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  of tier C, you have to stop selling websites
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  and start selling digital employees. Digital
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  employees. Wait, explain that? Think about the
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  mechanics of local business. A basic website
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  just sits there. It's static. A digital employee
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  actually does work. Okay. So instead of building
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  a generic page for a dentist, you use AI to build
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  a highly specialized portal that hooks directly
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  into their legacy booking software. Oh, I see.
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  Yeah, it cross -references their calendar, allows
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  patients to upload insurance cards, and automatically
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  verifies the data. Ah, so you're moving from
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  aesthetics to pure utility. Precisely. You have
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  to niche down. Target dentists or restaurants
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  specifically. If your site increases their actual
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  patient throughput by 20 % and saves their front
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  desk two hours a day, they do not care that you
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  built it with an AI agent in an afternoon. Right,
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  because they are paying for the operational result.
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  Exactly. But executing that requires understanding
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  how APIs work, how databases connect, and how
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  a specific industry operates. You aren't just
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  prompting anymore. You are integrating. And that
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  transition from just making things look pretty
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  to actually integrating backend systems is what
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  forces us out of Tier C and into Tier B? Yes.
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  The next step up. Right. Because if basic digital
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  products are completely commoditized, your only
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  defense is to move into the messy, complex back
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  end of a business. Tier B is entirely about solving
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  operational friction. And the demand here is
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  massive, but the technical stakes are obviously
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  much higher. So let's look at the AI Automation
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  Agency. This is where we start talking about
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  tools like N8n or Make. Every single business
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  on Earth bleeds money through administrative
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  friction. Oh, for sure. copying data from an
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  email, pasting it into a CRM, manually generating
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  an invoice, sending a Slack message. It's mind
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  -numbing. And error -prone. Highly error -prone.
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  So an AI automation agency comes in and strings
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  all those different apps together using APIs.
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  We're seeing market rates of anywhere from $500
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  to $2 ,000 just for the initial setup of one
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  workflow plus monthly support retainers. Which
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  is a very healthy business model. Yeah, there's
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  a real world example in our sources, a company
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  called Superintech that scaled from $5 ,000 to
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  $25 ,000 a month in revenue doing exactly this.
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  It's totally doable. But what does this all mean?
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  Let me push back here. If N8N is essentially
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  a visual drag and drop tool, why wouldn't a company
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  just have an intern watch a YouTube tutorial
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  and build the automation themselves? How does
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  this agency survive? Well, because drag and drop
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  doesn't mean unbreakable. Right. The mechanism
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  behind an automation is incredibly fragile. Let's
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  say the intern connects a Facebook lead ad to
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  a custom CRM using a webhook. It works beautifully
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  on day one. But on day 40, Facebook updates their
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  API payload structure. They rename the data field
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  from, you know, phone number to contact phone.
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  Oh, and the automation doesn't know that. Right.
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  It looks for phone number, finds nothing, throws
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  a silent error and just fails. For three days,
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  a client's sales team gets zero leads, the pipeline
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  dries up, and everyone panics. That is a very
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  expensive mistake for a business. Exactly. When
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  you run an AI automation agency, you are not
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  selling the drag and drop setup. You are selling
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  error handling, fault tolerance, and peace of
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  mind. I get it. You build systems that catch
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  those errors, alert you immediately, and reroute
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  the data so the client never misses a beat. Clear
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  results win over cheap tools. They pay the $2
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  ,000 fee and the retainer because the cost of
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  the automation breaking is exponentially higher
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  than your invoice. So you're basically acting
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  as digital infrastructure security. Basically,
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  yeah. Which leads perfectly into the second tier
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  B model, the AI receptionist agency. This uses
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  voice AI platforms like retail AI or VAPI. You
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  hook these up to N8n. and you deploy a voice
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  assistant that can handle after hours calls,
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  answer complex FAQs, and actually book calendar
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  appointments in real time over the phone. The
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  ideal targets here are businesses where a missed
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  call equals missed revenue. Home repair services,
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  dental clinics, high -end salons. Setup fees
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  here can easily reach up to $5 ,000. But the
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  catch and the risk profile on this is terrifying.
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  Oh, it's massive. If an automation breaks in
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  the background, maybe a spreadsheet doesn't update.
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  But if an AI receptionist hallucinates during
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  a live phone call with a customer, the damage
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  is public and immediate. A booking mistake by
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  an AI could cost you the client instantly. Well,
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  the technical challenge here is latency and hallucination.
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  For a voice AI to feel natural, the system has
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  to transcribe the human speech, process the intent
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  through a language model, synthesize a voice
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  response, and send the audio back, all in under
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  500 milliseconds. That's incredibly fast. It
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  has to be. If there is a lag, people just hang
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  up. And if the language model isn't strictly
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  confined to a specific knowledge base, it might
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  start offering unauthorized discounts or double
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  -booking time slots. You aren't just making a
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  technical error. You are actively sabotaging
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  their customer relations. But even if you solve
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  the latency and the hallucinations, there is
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  a massive existential threat to this specific
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  business model, isn't there? Yeah, the native
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  integration problem. Right. Huge software companies
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  like ServiceTitan and Weave, which these local
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  businesses already use to run their operations,
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  they're starting to build AI receptionists directly
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  into their native software. That's the hidden
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  risk we were talking about. If I run a plumbing
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  company and my core software suddenly offers
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  an AI receptionist for an extra 50 bucks a month,
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  I'm firing the external agency immediately. And
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  that is the fatal vulnerability of Tier B. You
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  are solving operational problems, but back office
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  operations are highly susceptible to being swallowed
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  by the big software providers. If you want a
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  truly defensible business, you have to get out
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  of the back office entirely. You have to move
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  closer to the cash register. Which brings us
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  to Tier A, the growth engines. Because if massive
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  software companies are eventually going to automate
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  the operational fixes, your only defense is to
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  focus on directly generating revenue. When you
• 13:05
  bring a company new customers, you become fireproof.
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  First up in tier A is the AI creative agency.
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  Historically, a creative agency required a small
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  army of copywriters, graphic designers and video
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  editors. Now, using image models like Mid Journey
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  or video generation tools like Runway and Kling
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  AI, a single operator can produce a volume of
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  high fidelity ad creatives that would have previously
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  taken a 20 person team a month to finish. And
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  the money's real. There's a case study in the
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  research a guy named Kevin who built an AI video
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  ads agency and hit $60 ,000 in monthly revenue
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  in just five months. That's wild. He even landed
• 13:45
  an $8 ,000 contract doing AI creatives for political
• 13:48
  campaigns and marketing firms. But wait, I have
• 13:51
  mid -journey on my phone right now. I can generate
• 13:54
  a cinematic image in 10 seconds. Brands have
• 13:56
  access to these exact same tools. Why on earth
• 14:00
  are they paying Kevin eight grand? Because generating
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  the image is the absolute easiest part of the
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  job. AI isn't the creative director, it's just
• 14:09
  the printing press. Okay, I like that analogy.
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  You can print a million gorgeous flyers in a
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  second, but if the psychological hook on the
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  flyer is garbage, no one is going to buy what
• 14:19
  you're selling. The value isn't the image, it's
• 14:21
  the ad performance. So it's about the mechanics
• 14:24
  of advertising, not the art. Exactly. Brands
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  don't want to sit around generating 50 slightly
• 14:29
  different variations of a video to see which
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  one has the best hook. They want to pay an expert
• 14:34
  who understands how to build a rigorous A -B
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  testing matrix. Right. You use the AI to rapidly
• 14:40
  generate dozens of variations. You run micro
• 14:43
  -budgets against them on social media. You analyze
• 14:45
  the click -through rates. And you isolate the
• 14:47
  single creative that lowers their customer acquisition
• 14:50
  cost. The client doesn't care about the AI. They
• 14:53
  care that you spent $1 ,000 of their ad budget
• 14:55
  to bring back $5 ,000 in sales. That makes a
• 14:58
  lot of sense. You have to prove your AI creatives
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  bring more clicks in sales. You're using AI for
• 15:03
  rapid iteration and data testing, not just pretty
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  pictures. Exactly. Which perfectly aligns with
• 15:10
  the other massive revenue driver in tier A, the
• 15:13
  AI lead generation agency. If you understand
• 15:15
  the mechanics of sales, this is arguably the
• 15:17
  most lucrative area to be in right now. Now let's
• 15:20
  get into the weeds on how this actually works
• 15:22
  because it's not just buying a list of 10 ,000
• 15:25
  emails and blasting out a generic, hello, would
• 15:27
  you like to buy my software message anymore?
• 15:29
  No, that doesn't work anymore. The sophisticated
• 15:32
  players are using a tool called Clay integrated
• 15:34
  with ChatGPT. Clay is fascinating because it's
• 15:37
  not just a database. It is an orchestration engine.
• 15:42
  Let's say you want to target software companies.
• 15:44
  Clay will scrape a target's LinkedIn profile.
• 15:47
  Then it uses what's called waterfall logic. It
• 15:49
  checks their recent job postings. It analyzes
• 15:52
  their current tech stack. It flags if they just
• 15:55
  raised a Series B funding round. It's gathering
• 15:58
  highly specific contextual ammo. Precisely. Once
• 16:02
  it has that hyper -specific data, it feeds those
• 16:05
  variables directly into a chat GPT prompt. The
• 16:08
  AI then writes a deeply personalized outreach
• 16:11
  email. It doesn't say, buy our software. It says,
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  Hey Sarah, I saw you just raised a Series B and
• 16:17
  you're actively hiring three new backend developers.
• 16:20
  Our tool specifically helps scale backend architecture
• 16:23
  during rapid team expansions. That is lethal.
• 16:27
  It is. The research mentions Tebo, who founded
• 16:30
  Squad, an AI -powered sales automation software.
• 16:33
  They rapidly hit $10 ,000 in monthly recurring
• 16:36
  revenue using these exact principles. Even though
• 16:39
  Squad is software, it proves the massive willingness
• 16:42
  to pay for AI sales tools. Absolutely. If you
• 16:46
  can automate the entire top of funnel sales process
• 16:49
  and deliver qualified meetings to a business
• 16:50
  owner's calendar, you hold the keys to their
• 16:53
  survival. They will never let you go. But there
• 16:55
  is a great stress test for this. If you are going
• 16:58
  to start an AI lead gen agency, you should use
• 17:01
  your own system to get your first client. If
• 17:04
  your sophisticated clay and chat GPT workflow
• 17:07
  can't convince a single business owner to take
• 17:09
  a meeting with you, you have absolutely no business
• 17:12
  charging someone else to use it. It is the ultimate,
• 17:15
  unavoidable proof of concept. Now, what if you
• 17:18
  were listening to this and thinking, I am deeply
• 17:20
  technical. I know how to build advanced automations.
• 17:23
  I understand API integrations. But I have zero
• 17:27
  interest in running ad campaigns or doing sales
• 17:30
  outreach. That pushes us to the final model in
• 17:33
  tier A, AI education and training. Right. Which
• 17:36
  involves teaching marketing teams to automate
• 17:39
  with an 8N or helping beginners build AI agents.
• 17:42
  But Here's where it gets really interesting.
• 17:46
  We are living in an era of absolute information
• 17:49
  abundance. I can go on YouTube tonight and watch
• 17:52
  100 hours of tutorials on N8n or Claude totally
• 17:55
  for free. True. So selling basic AI knowledge
• 17:58
  feels like trying to sell someone a bottle of
• 18:01
  tap water while they're standing next to a pristine
• 18:03
  river. It is exactly like selling tap water if
• 18:06
  all you are doing is giving a tour of the user
• 18:08
  interface. I mean, if you walk into a company
• 18:10
  and say, I will teach your team how to write
• 18:12
  better prompts and where the generate button
• 18:14
  is, you will be laughed out of the room. Yeah,
• 18:16
  nobody's paying for that. You have to understand
• 18:18
  the transition from teaching features to teaching
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  transformations. You can only charge if you teach
• 18:24
  them how to solve real problems. Give me a concrete
• 18:27
  example of that transition. Well, free content
• 18:30
  teaches you what a hammer is and maybe how to
• 18:32
  swing it. Paid education teaches you how to build
• 18:35
  a house. I like that. You cannot sell abstract
• 18:40
  knowledge. You have to sell a working end -to
• 18:43
  -end project. Instead of offering a generic AI
• 18:46
  for beginners course, you approach a customer
• 18:49
  support team. You say, I am going to run a two
• 18:52
  -day hands -on workshop. By the end of those
• 18:55
  two days, we will have built a custom AI agent.
• 18:58
  securely connected to your internal knowledge
• 19:00
  base that automatically drafts responses to your
• 19:03
  tier one support tickets. It will save your team
• 19:05
  30 hours a week starting on Friday. So you aren't
• 19:08
  selling the technology. You're selling the structured
• 19:11
  implementation of the technology to solve their
• 19:14
  specific messy reality. Yes. The value is in
• 19:18
  bridging the gap between theoretical AI capabilities
• 19:20
  and their chaotic corporate data. And that concept,
• 19:24
  bridging the gap between the technology and the
• 19:26
  larger business reality, is what launches us
• 19:29
  to the absolute peak of this entire ecosystem.
• 19:31
  We have arrived at tier S. The strategic peak.
• 19:35
  This is where we move entirely away from being
• 19:37
  the doer and become the thinker. Because the
• 19:40
  ultimate value in the AI economy isn't knowing
• 19:42
  how to execute a task. It's knowing which task
• 19:45
  actually needs executing in the first place.
• 19:48
  This begins with AI consulting and audits. Most
• 19:51
  established multi -million dollar companies are
• 19:53
  in a state of quiet panic right now. The executives
• 19:56
  see the headlines, they feel the pressure from
• 19:58
  their board of directors to implement AI, but
• 20:02
  they have no functional idea where to start.
• 20:05
  So an AI consultant doesn't go in and write code.
• 20:08
  They review how companies work to find costly
• 20:10
  problems AI can fix. We have a case study of
• 20:13
  Simon Bergeron's business Lemonbrand, which reached
• 20:16
  $40 ,000 to $50 ,000 a month doing this. The
• 20:19
  mechanics of his pricing are brilliant. They
• 20:21
  really are. He charges between $1 ,500 and $1
• 20:23
  ,500 just for an audit, and then monthly consulting
• 20:26
  starts at around $2 ,500. Let's break down how
• 20:28
  an audit actually functions. You go into a company
• 20:31
  and you map their processes. You interview the
• 20:34
  department heads. You might find out that, say,
• 20:37
  the supply chain team is spending 40 % of their
• 20:40
  week manually cross -referencing shipping spreadsheets
• 20:43
  against vendor emails. Such a common problem.
• 20:45
  Right. You document the financial cost of that
• 20:47
  waste of time. Then you present a comprehensive
• 20:50
  architectural plan showing exactly how a specific
• 20:54
  AI workflow could eliminate that bottleneck.
• 20:56
  You prove the return on investment on paper before
• 20:58
  you ever ask for the massive implementation contract.
• 21:01
  Exactly. And the advice here for you listening
• 21:04
  is to start with a small audit of one process
• 21:06
  to prove the value of a larger project. And if
• 21:10
  you scale that high -level consulting model to
• 21:12
  its absolute logical conclusion, you reach the
• 21:15
  final most lucrative business idea in our analysis,
• 21:18
  the Fractional Chief AI Officer, or CAIO. If
• 21:23
  we connect this to the bigger picture, this is
• 21:25
  where deep, traditional business acumen completely
• 21:28
  supersedes technical coding skills. A fractional
• 21:32
  CAIO is an executive role, usually operating
• 21:35
  on a part -time or contract basis across multiple
• 21:37
  companies. leading AI strategy without being
• 21:40
  full time, deciding where to use AI, managing
• 21:43
  teams, checking ROI. John Chaney is the prime
• 21:47
  example here in our sources. He generated over
• 21:49
  $2 million in his first year offering fractional
• 21:52
  AI leadership. But let's be clear about what
• 21:55
  he is actually doing. He is not in the trenches
• 21:57
  debugging Python code. Not at all. When a company
• 22:00
  is preparing to invest hundreds of thousands
• 22:02
  of dollars into overhauling their entire corporate
• 22:06
  infrastructure, they are dealing with massive
• 22:08
  risk. They are investing serious money. They
• 22:11
  need a trusted leader to oversee the transition,
• 22:14
  someone to develop the strategy, vet the third
• 22:16
  party software vendors to ensure data security,
• 22:19
  and most importantly, manage the human element.
• 22:22
  The human element, like employees pushing back.
• 22:25
  Exactly. Change management is arguably the hardest
• 22:28
  part of AI integration. You have entire departments
• 22:31
  terrified that this new technology is going to
• 22:33
  replace their jobs. They will actively resist
• 22:37
  adopting the tools. Of course they will. It's
• 22:39
  human nature. The fractal CAIO has to navigate
• 22:42
  that corporate politics, alleviate those fears
• 22:45
  through structured training, and align the entire
• 22:48
  technological shift with the financial goals
• 22:50
  demanded by the board of directors. It is a profoundly
• 22:53
  psychological and strategic role. You cannot
• 22:57
  do this if you are just a 22 year old who watched
• 22:59
  a lot of automation tutorials. You need battle
• 23:02
  tested executive experience. So synthesizing
• 23:06
  this entire matrix for you listening right now,
• 23:09
  your path depends entirely on the capital and
• 23:12
  experience you currently hold. If you are a beginner
• 23:15
  starting from zero, the mechanics are clear.
• 23:17
  Start in tier C or B. build a specialized booking
• 23:20
  website or a hyper -specific end -to -end automation
• 23:23
  and go sell it manually to one local business.
• 23:26
  Get that first client. Understand the friction.
• 23:29
  If your background is already in marketing or
• 23:31
  sales, you should lean into tier A. Do not waste
• 23:34
  time building operational workflows. Build an
• 23:37
  AI creative agency or an AI Legion engine. And
• 23:40
  for the AI experts, lean into education. Right.
• 23:44
  But if you have a decade of deep corporate management
• 23:46
  experience, Do not start at the bottom building
• 23:49
  websites. Leverage your executive presence. Move
• 23:52
  straight into tier S. Become the translator between
• 23:55
  the board's financial goals and the technical
• 23:57
  reality as a consultant or fractional CAIO. The
• 24:01
  action plan is to pick just one of these models
• 24:03
  that matches your skills. Test it with a small
• 24:06
  contained project and see what people will actually
• 24:09
  pay for. The market is the only feedback loop
• 24:11
  that ultimately matters, but As we wrap this
• 24:14
  up, I do want to leave you with one final slightly
• 24:18
  unsettling thought to mull over. All right, lay
• 24:20
  it on us. We noted that the fractional CAIO sits
• 24:24
  at the peak of tier S because it requires high
• 24:26
  level human strategic reasoning. But look at
• 24:29
  the trajectory of the foundational models. They
• 24:32
  are rapidly evolving from simply executing tasks
• 24:35
  to developing their own autonomous reasoning
• 24:37
  capabilities. The agents are starting to think.
• 24:40
  So the question is, As AI models advance to the
• 24:44
  point of developing their own strategic reasoning,
• 24:47
  how long until the chief AI officer is successfully
• 24:50
  modeled and replaced by the AI itself? Will the
• 24:53
  ultimate AI business of the future even need
• 24:56
  a human executive, or will it simply be an AI
• 24:58
  running the entire business completely on its
• 25:00
  own? Now, there is a terrifying and fascinating
• 25:04
  mechanism to chew on. We'll leave you to explore
• 25:06
  that one on your own.