Latest / Today’s AI News / AI Agents Are Running Businesses Now, The A2A Future of B2B, The One-Person Billion Dollar Company

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AI Agents Are Running Businesses Now, The A2A Future of B2B, The One-Person Billion Dollar Company · Today’s AI News

• 0:00
  Welcome back to today's AI News, brought to you by 9X
• 0:02
  Productions. Let's just unpack this right
• 0:05
  away. Yeah, let's get into it.
• 0:06
  Imagine your AI used to be a really fast typing intern.
• 0:10
  You know someone who could summarize a meeting or draft a
• 0:12
  quick e-mail for you. Right.
• 0:14
  Helpful but basic. Exactly.
• 0:16
  But now imagine you walk into the office on a Monday morning
• 0:20
  and that intern just got promoted to chief operating
• 0:23
  officer and they're running the whole department while you
• 0:27
  sleep. It's a completely different ball
• 0:29
  game now. It really is.
• 0:31
  Welcome to the deep dive. Our mission today is to explore
• 0:35
  exactly what happens when we cross that line, when we move
• 0:38
  from artificial intelligence acting as a simple assistant, to
• 0:41
  AI functioning as a fully autonomous operator.
• 0:44
  Which is the biggest shift we've seen in years?
• 0:46
  Totally. And for our latest developments
• 0:48
  today, we are pulling this all apart using an exclusive Q&A
• 0:51
  from March 29th, 2026 with Ku Zhang.
• 0:55
  He's the President of alibaba.com.
• 0:57
  Right. And we're looking at that
• 0:58
  alongside the technical briefs for their new agentic system,
• 1:01
  which is called Ashio Work. So to really grasp the sheer
• 1:04
  scale of what Zhang is talking about, we kind of have to look
• 1:07
  at the underlying architecture that even got us here over the
• 1:10
  last year. Yeah, definitely.
• 1:12
  Because if you've been tracking the deployment of Agentic AI,
• 1:16
  you know that the release of the Open Claw framework just a few
• 1:19
  months ago, yeah, it fundamentally shifted the
• 1:21
  landscape. It was a massive deal.
• 1:22
  Well. Absolutely.
• 1:23
  I mean, Open essentially provided this universal
• 1:28
  standardized protocol for AI models to securely call external
• 1:32
  APIs, and it lets them manipulate software environments
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  without any human middleware. Which means no human clicking
• 1:39
  the buttons in between steps. Exactly, it took agentic
• 1:42
  behavior from being this really neat theoretical research paper
• 1:46
  to actual real world deployment. We are no longer talking about
• 1:50
  chat bots here. We are looking at digital
• 1:52
  employees handling complex multi step, cross-platform tasks 24/7.
• 1:57
  But I mean, you're talking about handing over the keys to the
• 1:59
  operational engine of a company, yes?
• 2:01
  You can't just plug a quote UN quote CEO level intelligence
• 2:05
  into an old business model and expect it to function smoothly,
• 2:08
  right? No, not at all.
• 2:09
  The organizational psychology of that is just massive.
• 2:12
  I mean, how do you even begin to trust a system with that kind of
• 2:16
  unchecked power? Well, Zhang points out that the
• 2:18
  mindset shift begins by totally divorcing ourselves from being
• 2:21
  impressed by mere activity. We have to focus strictly on
• 2:25
  value. OK, Unpack that a little bit,
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  Activity versus value. Right, so for the last few years
• 2:30
  the tech world has been completely dazzled by the sheer
• 2:33
  speed of generation. Like how fast a model can write
• 2:37
  Python code, or you know, process a 10,000 row
• 2:40
  spreadsheet. Or spit out a whole marketing
• 2:42
  campaign in 10 seconds. Exactly.
• 2:44
  But for an autonomy autonomous operator, those are just base
• 2:47
  level utilities. Generating text or code is no
• 2:50
  longer the metric for success. Interesting.
• 2:52
  Yeah, the true measure of an agentic system is its ability to
• 2:56
  navigate high value decisions, route around unexpected
• 2:59
  roadblocks, and actually Dr. measurable profit.
• 3:02
  That makes perfect sense actually, because if you hired a
• 3:05
  human COO and their only skill was typing really fast, I mean
• 3:09
  they'd be fired by lunch. Right.
• 3:10
  You want them making strategic decisions.
• 3:12
  Exactly. But handing over strategy to an
• 3:15
  algorithm that requires some serious guardrails.
• 3:19
  Like if an AI decides to aggressively optimize a supply
• 3:23
  chain, it could theoretically break three other departments in
• 3:26
  the process just by moving too fast.
• 3:28
  Which is exactly why the architecture relies heavily on
• 3:31
  what Zeng calls checkpoints. Checkpoints.
• 3:33
  Yeah, because handing over core operations requires radical
• 3:37
  transparency, but not in the way we usually think about like
• 3:41
  explainable AI. Right.
• 3:43
  You don't just ask the AI, hey, what are you thinking?
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  No. You force the system to
• 3:46
  breakdown complex tasks into deterministic logic gates.
• 3:50
  OK, see, the AI is operating probabilistically right?
• 3:54
  It's basically guessing the best next step.
• 3:57
  But before it executes a major action, it must output its
• 4:00
  reasoning into structured data that hits a checkpoint.
• 4:03
  Oh wow, so it has to prove it. It essentially has to
• 4:05
  mathematically prove its work before the system allows it to
• 4:08
  proceed to the next node in the workflow.
• 4:10
  That's a huge safeguard. It is the goal is to ensure a
• 4:13
  minor digital hallucination doesn't just butterfly affect
• 4:16
  its way into a catastrophic real world supply chain collapse.
• 4:20
  So what is this? Does this actually mean for the
• 4:23
  human beings who used to, you know, manage that supply chain?
• 4:26
  That's a big question. It sounds like you are no longer
• 4:30
  the quarterback out on the field, reading the defense,
• 4:33
  throwing the ball and taking all the physical hits.
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  Instead, you've become the head coach.
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  You're standing safely on the sidelines, drawing up the
• 4:40
  playbook, acting as the referee, and basically setting the strict
• 4:44
  standards for how the game should be executed.
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  And what's fascinating here is how that dynamic completely
• 4:49
  flips the traditional narrative of AI replacing human workers.
• 4:53
  Really. How so?
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  Well, at this specific stage of the technology's maturity,
• 4:59
  people aren't being replaced, they are being upgraded to a
• 5:02
  supervisory layer. Oh I like that upgraded.
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  Yeah, Zhang makes it abundantly clear that human workers have to
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  transition from doing the grunt work of execution to designing
• 5:12
  the logic of the operation. Because everyone has the AI now.
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  Exactly, Having the smartest AI is no longer a competitive Moat
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  because through API access literally every company on earth
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  has access to frontier models. Right, the playing field is
• 5:26
  totally levelled. So the true competitive
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  advantage will belong to the humans who can identify the
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  specific bottlenecks in their business and then design the
• 5:35
  perfect frictionless workflows around those goals.
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  So you're upgrading from the person doing the heavy lifting
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  to the architect of the entire system?
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  Exactly. But if I'm stepping into that
• 5:45
  coach role, a coach still needs a team to manage.
• 5:48
  You can't just coach thin air. Very true.
• 5:51
  And that is where the rubber meets the road with
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  whatalibaba.com has actually built with at you work.
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  I mean, we have the theory, but how does the platform actually
• 5:59
  manifest this digital workforce? So at you work basically
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  operates as an orchestration platform.
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  It deploys A dynamic fleet of specialized agents, and they're
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  powered by Alibaba's Quinn models.
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  And why Quinn specifically? Is there a reason they went with
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  that? Yes, and the reason is critical.
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  These models aren't just optimized for like
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  conversational fluency or chatting.
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  They are natively trained on tool calling and API routes.
• 6:23
  Oh, so they're built to take action from the ground up?
• 6:26
  Exactly. And the platform assembles these
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  agents based entirely on user described goals using a no code
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  interface. Wait, no code so I don't need to
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  know Python to set this up. Not a single line.
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  You simply describe the business outcome you want to achieve, and
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  the system dynamically pieces together the right combination
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  of specialized agents, software, connectors, and skills to just
• 6:47
  get it done. But wait.
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  How do I actually teach an AI my specific expertise if the whole
• 6:52
  thing is no code? Right.
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  It sounds a bit vague at first, yeah.
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  I just typing out like a messy Word document of my personal
• 7:00
  business rules and hoping the AI just sort of understands the
• 7:03
  vibe. Not quite.
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  You are building what's essentially A semantic logic
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  tree, but you're doing it using natural language.
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  Under the hood, the system translates your plain English
• 7:12
  instructions into immutable constraints.
• 7:15
  Give me an example of that. Sure.
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  Say you have a highly effective proprietary method for
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  qualifying a sales lead. You tell the system never accept
• 7:23
  a wholesale price above X unless shipping is included and always
• 7:27
  verify the suppliers latency. OK, that's pretty specific.
• 7:30
  Right. And the Actua work platform
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  takes that and converts that sentence into strict API
• 7:35
  parameters. That logic becomes a permanent
• 7:39
  reusable skill module in your digital workforces playbook.
• 7:42
  Oh wow. So you can swap agents in and
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  out all day, but you're encapsulated expertise remains
• 7:48
  the governing law of that workflow.
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  Let's ground this for the listener with the specific
• 7:53
  e-commerce example Zhang actually provided in the Q&A,
• 7:57
  because the mechanics of this are wild.
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  It's a great example. So you tell the actual work
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  system you want to launch a business.
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  The agent goes out, pings various data brokers and
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  analyzes real time market trends just to select a trending
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  product. Right, doing all the market
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  research instantly. Yes, then it uses its API access
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  to automatically spin up a storefront.
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  I mean formatting the database, setting up the UI and publishing
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  it on Shopify, Amazon or TikTok shop with literally one click.
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  And as impressive as that digital storefront is, the true
• 8:27
  operator definition really kicks in on the back end.
• 8:29
  Oh yeah, the sourcing. Exactly once the product is
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  selected, the sourcing agent autonomously reaches out to
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  global suppliers. It initiates multi round
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  negotiations to secure the best wholesale price.
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  It's haggling for you. It is actively reading the
• 8:45
  suppliers counter offers, adjusting its bids based on the
• 8:48
  margin requirements you set in your logic tree, and haggling
• 8:51
  over minimum order quantities. That is insane.
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  And it only surfaces the final optimized deal to you for
• 8:57
  approval once the negotiation has completely plateaued.
• 9:00
  But here's where it gets really interesting to me.
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  If my AI is conducting multi round supplier negotiations and
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  the supplier is likely a modern tech forward business using
• 9:11
  their own agentic software. Which they probably are.
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  Right, so how long until this is just two AIS haggling over the
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  price of wholesale goods? Are they going to get trapped in
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  some infinite loop where my AI low balls their AI by a fraction
• 9:24
  of a send every millisecond and their AI keeps citing micro
• 9:27
  fluctuations and supply chain constraints?
• 9:29
  It's funny you mentioned that because that specific scenario,
• 9:33
  two intelligent systems deadlocking in a high frequency
• 9:36
  negotiation work. It is a recognized
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  vulnerability. I knew it.
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  So how do they stop that? To prevent it, the interaction
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  protocol actually requires standardizing the negotiation
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  parameters. The AI's aren't haggling
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  endlessly like humans might. They're exchanging structured
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  data arrays that represent their hard limits, their constraints,
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  and volume discounts. Ah, so it's just math to them.
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  Exactly. They calculate the optimal
• 10:02
  overlap in a matter of seconds and notice what happened to the
• 10:05
  human in that entire e-commerce pipeline.
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  You did almost nothing. Right.
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  You didn't build the website, You didn't research the trends.
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  You didn't write a single e-mail to the supplier.
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  Your role shrank entirely to the final cryptographic sign off.
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  Wow. You approve the initial payment,
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  and later you approve the regulatory submissions.
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  You've officially moved from managing the steps to defining
• 10:26
  the outcome. Which bridges perfectly into Ku
• 10:29
  Zhang's exact prediction for the next five years.
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  Going back to the idea of a is negotiating with a is.
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  Zhang explicitly states that the future of B2B commerce is
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  entirely A to a agent to agent. The agent to agent economy is
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  honestly the logical endpoint of this entire architecture.
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  Within five years, Zhang anticipates that the vast
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  majority of routine business to business tasks will flow
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  autonomously between agents. Human initiation will be
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  practically nonexistent. So it's just happening in the
• 11:01
  background. Totally.
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  Your inventory agent realizes a specific Stu is running low, it
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  automatically pings your suppliers fulfillment agent.
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  They negotiate the replenishment terms based on preset structured
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  parameters, draft the digital contract, and then just route
• 11:17
  the finalized payment order to a human for biometric approval.
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  I have to challenge the reality of that, though I really do go
• 11:23
  for it, Zhang says. The human takes the blame if
• 11:25
  something goes wrong, right? Yes, he's very clear on that.
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  But let's be real about the sheer speed of compute.
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  If an A to a transaction happens in milliseconds, and my agent
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  agrees to a terrible dynamic pricing contract that bankrupts
• 11:38
  my company before I've even poured my morning coffee, is a
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  judge really going to care that I was, quote UN quote,
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  technically the head coach? The speed of A to a commerce
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  outpaces human oversight by design.
• 11:52
  How is that actually tenable from a liability standpoint?
• 11:55
  That is the ultimate accountability bottleneck, and
• 11:57
  it is exactly why these systems are not allowed to execute
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  critical final actions synchronously.
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  What do you mean by synchronously?
• 12:05
  So to make the a 2A economy legally and financially safe,
• 12:08
  agents operate in tightly sandboxed environments with
• 12:11
  asynchronous approval gates. OK, asynchronous, so there's a
• 12:14
  pause. Exactly.
• 12:15
  Your agent and the supplier's agent can negotiate the draft of
• 12:18
  that contract in milliseconds, yes, but the system places the
• 12:21
  finalized contract into an ESPRO like hold state.
• 12:25
  Ah, OK, that makes sense. Yeah, the cryptographic
• 12:28
  signature that legally binds your company requires a manual
• 12:31
  token. It requires your explicit human
• 12:34
  approval. Zhang is adamant about this.
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  If you blindly click approve on a costly mistake without
• 12:41
  reviewing the summary checkpoint, the responsibility
• 12:44
  stays entirely with you. So you can't just point fingers
• 12:48
  at the tech. No, you cannot blame the
• 12:50
  algorithm in court because the algorithm did not authorize the
• 12:53
  actual execution. It really reminds me of the
• 12:55
  autopilot system on a commercial jet.
• 12:57
  Oh, that's a good comparison, right?
• 12:59
  Because a modern jet can fly itself entirely across the
• 13:02
  Atlantic Ocean, It constantly adjusts for wind shear, it
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  maintains altitude, and it optimizes fuel consumption far
• 13:08
  better than a human physically could.
• 13:10
  Without a doubt. But the human pilot is still
• 13:12
  sitting in that seat, legally responsible for the lives on
• 13:14
  board. Yeah.
• 13:15
  They oversee the instruments. They monitor the systems, and if
• 13:18
  there's sudden catastrophic turbulence, they take the yoke.
• 13:21
  The plane flies, but the pilot commands.
• 13:24
  That analogy perfectly captures the dynamic.
• 13:27
  Autonomy and accountability are not mutually exclusive.
• 13:29
  They have to coexist. The architecture of actual work
• 13:32
  dictates that any action involving private files, real
• 13:35
  financial transactions, or external legal consequences, it
• 13:39
  just cannot bypass that human safety valve.
• 13:42
  And Zhang gave a really great example of this with VAT
• 13:45
  filings, value added tax. Oh yeah, tax compliance is huge.
• 13:49
  Actually, work can do all the heavy lifting.
• 13:51
  It scrapes the local tax regulations, which, you know,
• 13:54
  vary wildly by jurisdiction. It's a nightmare for humans to
• 13:57
  do manually. Total nightmare.
• 13:59
  It structures all your unstructured financial data,
• 14:02
  mapping your revenue to the correct tax codes, which
• 14:04
  drastically reduces the manual accounting burden.
• 14:07
  But the agent physically cannot submit the final filing to the
• 14:11
  government portal. That submission must go through
• 14:14
  an authenticated human channel. Because the core value of a
• 14:17
  human running an agent driven business is no longer execution,
• 14:21
  it is the exercise of judgement. You are the ultimate arbiter of
• 14:24
  risk. OK.
• 14:25
  So if human judgement, strategic risk assessment and just
• 14:28
  generally signing off on checkpoints are the primary
• 14:31
  roles left for us, how does this change the way we actually build
• 14:35
  a career? That is the big societal shift
• 14:37
  right there. Because if the AI is doing all
• 14:39
  the work, how does a company value a human employee's skills
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  anymore? This brings us to a profound
• 14:45
  structural change Zhang points out.
• 14:47
  Regarding the new professional edge.
• 14:49
  He states definitively that headcount is no longer a proxy
• 14:53
  for organizational capability. Elaborate on that.
• 14:56
  Well, historically the power of a corporation was measured by
• 14:59
  its human capital, right? A company with 10,000 employees
• 15:03
  could effortlessly crush a company with 10 purely through
• 15:07
  brute force execution. Right, they just had more hands
• 15:10
  on deck. Exactly.
• 15:11
  But with agentic platforms standardizing execution, that
• 15:15
  metric is completely dead. Because a solo founder utilizing
• 15:19
  an agent platform like Atchio Work can basically mimic the
• 15:23
  operational footprint of a massive enterprise.
• 15:25
  Yes, entirely on their own. They can handle global sourcing,
• 15:29
  multi market regulatory compliance, and localized
• 15:31
  marketing all simultaneously. They are effectively competing
• 15:35
  with multinational corporations that have, you know, entire
• 15:38
  floors of skyscrapers dedicated to those specific tasks.
• 15:41
  Precisely So what makes a human inherently valuable in a 0
• 15:46
  execution environment, right? What's left?
• 15:48
  Zhang argues that the highest value professionals will be
• 15:51
  those with incredibly deep, nuanced domain expertise.
• 15:55
  It's no longer about knowing the mechanical steps of how to do
• 15:58
  the work, it's about knowing what exceptional looks like.
• 16:01
  Oh, I love that phrasing. Yeah.
• 16:02
  You need the expertise to set the precise success criteria for
• 16:06
  the AI's logic tree, and more importantly, you need the
• 16:10
  trained eye to spot the subtle, complex errors an AI might make
• 16:16
  before you give that final sign off.
• 16:18
  It's the difference between knowing how to operate a
• 16:19
  camera's shutter and knowing what makes a compelling,
• 16:22
  emotionally resonant photograph. That's spot on.
• 16:25
  The AI can pull focus perfectly, but you still have to direct the
• 16:28
  shoot. And Zhang mentioned something
• 16:30
  else about this domain expertise that completely shifts the labor
• 16:33
  model. Yeah, he said.
• 16:34
  This expertise becomes a portable asset.
• 16:37
  This is the real paradigm shift for modern careers.
• 16:40
  In the traditional economy, your skills were intrinsically tied
• 16:43
  to your labor. You traded your physical time
• 16:45
  for a salary. Right hours for dollars.
• 16:47
  But if you can encapsulate your domain expertise into reusable,
• 16:51
  usable AI skills like we discussed with those semantic
• 16:54
  logic trees, that expertise becomes a standalone product.
• 16:58
  Wait, so you can sell it? Exactly.
• 17:00
  It can be packaged into a proprietary module.
• 17:03
  It can be monetized in a digital marketplace.
• 17:06
  You can take your custom trained agent workflows and license them
• 17:09
  to multiple employers simultaneously.
• 17:12
  You're not renting out your hours anymore, you're licensing
• 17:14
  your digital playbook. Yes.
• 17:16
  That is an incredible reframing of human capital, and it
• 17:20
  perfectly leads us into our next section.
• 17:22
  Let's cover our quick hits and everything else in AI today.
• 17:24
  Yeah, let's jump into the lightning round from the
• 17:26
  interview. Toward the end of the Q&A, they
• 17:29
  put Zhang through this Rabbit Fire section, and there's some
• 17:32
  critical insights here about where the technology is actually
• 17:34
  headed. First, they asked him to
• 17:37
  identify the biggest misconception regarding AI
• 17:39
  agents. And his answer perfectly
• 17:41
  validates our entire discussion today.
• 17:43
  The prevailing fear is that AI agents are designed to replace
• 17:46
  people entirely. Which everyone is worried about.
• 17:49
  Of course, But he stresses that the architecture is actually
• 17:52
  designed to act as a dedicated team of experts Co constructing
• 17:56
  workflows alongside humans. It's an iterative, collaborative
• 17:59
  feedback loop, not just a magic button that renders humans
• 18:03
  obsolete. They also pressed him on what
• 18:06
  capability AI still fundamentally lacks, and he
• 18:09
  pointed specifically physical grounding.
• 18:12
  Which makes sense. Yeah, when you think about it,
• 18:14
  it is the great divide. Right now, AI reasoning is
• 18:17
  scaling exponentially in the digital realm.
• 18:20
  You can analyze massive data sets, write complex software,
• 18:23
  negotiate contracts at the speed of light.
• 18:25
  But it can't fix a sink. Exactly.
• 18:27
  Bridging that digital intelligence into real world
• 18:30
  physical execution. Like an AI physically navigating
• 18:33
  A chaotic warehouse to pack a customized box, or a robot
• 18:37
  diagnosing and fixing a leaking pipe, That translation from bits
• 18:40
  to atoms is still a massive unresolved bottleneck.
• 18:43
  And that gap between digital capability and physical
• 18:46
  imitation raises A vital question about where the
• 18:48
  immediate financial value lies in the tech sector.
• 18:50
  Which brings us to a somewhat spicy take from Zhang.
• 18:53
  Yeah, this leads directly to his most controversial belief.
• 18:57
  From the interview, he noted that a huge portion of the tech
• 19:00
  industry is hyper focused on consumer apps, building sleek
• 19:03
  tools designed for the general public.
• 19:05
  Right, everyone wants the next viral app.
• 19:07
  But he bluntly stated that CN or consumer end apps are
• 19:11
  fundamentally just about killing time.
• 19:13
  BN or business tools are about making money.
• 19:16
  Wow, shots fired. Yeah, Alibaba is clearly
• 19:20
  planting its flag in the B to B space, betting billions that the
• 19:24
  real revolution and the real profit is an enterprise
• 19:27
  efficiency, not consumer entertainment.
• 19:30
  Which brings us to his final prediction.
• 19:31
  And honestly, the implications of this one are just staggering.
• 19:35
  They asked him how far away we are from seeing the first one
• 19:38
  person billion dollar company. A true solo Unicorn.
• 19:42
  And his response? Probably just months.
• 19:44
  It forces a complete recalibration of what is even
• 19:46
  possible in business. I really want you, the listener,
• 19:49
  to pause and visualize the mechanics of that a single
• 19:53
  person sitting at a desk with an idea in a finely tuned fleet of
• 19:57
  autonomous agents. No HR department to manage
• 20:00
  conflicts. No middle management to relay
• 20:02
  instructions, no payroll tax to calculate and no commercial
• 20:07
  office space to rent. Just pure execution, just.
• 20:10
  Pure, unadulterated execution of our idea at a global scale,
• 20:15
  achieving a billion dollar valuation in months without
• 20:19
  hiring a single human employee. It shatters every fundamental
• 20:23
  law of business scaling we've relied on for the last century.
• 20:26
  It absolutely breaks the old models and it forces us to
• 20:30
  critically evaluate our own positions within this new
• 20:32
  economy. The immediate question for
• 20:34
  anyone listening is how can you start abstracting your unique
• 20:38
  domain expertise into these reusable workflows right now?
• 20:42
  This is essentially the dawn of community AI workflows where
• 20:45
  people will build, share, and license their encapsulated
• 20:48
  expertise. If the mechanical execution of
• 20:51
  your job is being automated, you need to identify what your
• 20:53
  specific high value playbook is and figure out how to codify it
• 20:57
  before the market simply expects you to bring your own agents to
• 21:00
  the table. Because if you don't define your
• 21:01
  playbook, you're just going to be the fast typing intern trying
• 21:05
  to out type the digital COO, and that is a race you will
• 21:08
  mathematically lose. Which leaves us with a totally
• 21:11
  different, much larger question to consider as we wrap up.
• 21:15
  Let's hear it. If we are truly months away from
• 21:18
  a one person billion dollar company, what happens to the
• 21:22
  macro economy? For centuries, wealth creation
• 21:26
  was inextricably linked to job creation.
• 21:29
  A company grew, it hired more people, and wealth was
• 21:32
  distributed through salaries which funded local economies and
• 21:35
  tax bases. Right.
• 21:36
  If a solo founder can generate a billion dollars in value with 0
• 21:40
  employees, how do government governments tax that
• 21:42
  effectively? How does society distribute
• 21:45
  resources when the concept of business success is completely
• 21:48
  divorced from job creation? That is the multi trillion
• 21:51
  dollar question and it paints a picture of a future that is
• 21:54
  equal parts thrilling and terrifying.
• 21:56
  So take some time today to reflect on what your specific
• 21:59
  domain expertise truly is. What is the one thing you know
• 22:02
  how to do better than the AI? The area where your judgement of
• 22:06
  what exceptional looks like just cannot be replicated by a logic
• 22:09
  tree because that is your new most valuable asset.
• 22:11
  Thank you so much for joining us on this deep dive.
• 22:13
  If you want to make sure you're always staying ahead of seismic
• 22:15
  shifts like this, hit that subscribe button to stay
• 22:18
  updated. And please take a second to rate
• 22:19
  the show 5 stars. It really helps us keep bringing
• 22:22
  you these deep dives. Remember, the intern just got
• 22:25
  promoted to COO. It's time to figure out what
• 22:29
  kind of head. Coach you want to be.
• 22:30
  We'll see you. Back here for tomorrow's
• 22:32
  episode.