Deploy AI Bots Replaces SMB Staff With Autonomous Agent Swarms

Stop Hiring: Build an AI Workforce Instead With Deploy AI Bots — Ai4

Ai4

Business growth depends on deploying autonomous agent swarms against discrete operational bottlenecks rather than building frontier models, rendering traditional SMB hiring obsolete.

AI-assisted summary of Stop Hiring: Build an AI Workforce Instead With Deploy AI Bots — original source. Check the original for full context.

Source published: 2026-08-18

Detailed notes

## Background Published on YouTube by Ai4 on August 18, 2026, this presentation features Joel Yi, Founder and CEO of Deploy AI Bots. According to the page description, Yi draws on his experience training over 300 companies to demonstrate how small and medium-sized businesses can move beyond basic chatbots and deploy autonomous AI agent workforces to run operations, sales, and customer support end-to-end.

## TL;DR Joel Yi presents a blueprint for replacing conventional hiring with autonomous AI agent workforces, opening with a demonstration of a primary Hermes agent spinning up specialized sub-agents without human intervention. To illustrate production viability, he deconstructs an automated DM setter and scheduling receptionist running across a 200-location vehicle tinting franchise via GoHighLevel and Shopmonkey. He explains that robust production agents rely on three practical pillars: the reasoning Brain, tool-executing Hands and Arms, and behavioral Guardrails codified in simple configuration files like `SOUL.md`. Finally, Yi urges founders to prioritize distribution and business outcomes over technical perfection, showing how targeting unglamorous, high-value bottlenecks—such as legal demand letter drafting—yields high-margin enterprise agency contracts.

## Atmosphere & style The presentation carries an energetic, direct, and demystifying tone, opening with Yi commanding the room's full focus as he walks attendees through a silent, fast-paced deployment GIF. It feels part technical masterclass and part no-nonsense founder playbook, balancing personal credibility from his cybersecurity and engineering background with sharp commercial pragmatism. The delivery is punchy and accessible, cutting through artificial intelligence hype to leave business owners feeling equipped and urgently motivated to deploy autonomous tools.

## Key takeaways - [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) Autonomous AI agents have progressed to the point where they can orchestrate, configure, and spawn other specialized agents without human intervention. - [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) Self-learning and self-healing multi-agent swarms represent an operational shift toward practical artificial general intelligence (AGI). - [02:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=120s) Software development timelines have collapsed drastically; complex machine learning projects that required six months in 2018 can now be reproduced within 24 hours using modern LLM tools. - [02:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=120s) Training underserved youth in agent engineering provides direct economic mobility, raising earning potential and serving as a safeguard against trafficking. - [02:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=120s) Small and medium-sized businesses (SMBs) offer the highest-leverage, immediate opportunity for autonomous agent deployments compared to slow-moving enterprise systems. - [03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) Production-grade AI agents can autonomously execute end-to-end commercial workflows, including customer qualification, pricing estimation, payment processing, and calendar scheduling. - [03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) Agent behaviors, objection-handling logic, and escalation triggers can be effectively orchestrated using simple, plain-text configuration files like SOUL.md. - [03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) Every production AI agent architecture balances three core components: the Brain (LLM reasoning), the Hands and Arms (execution tooling and APIs), and Guardrails (deterministic boundaries and fallback protocols). - [03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) Job displacement from AI is driven primarily by individuals who master building and deploying AI agents replacing those who do not. - [07:31](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=451s) Market distribution and concrete business outcomes matter more for commercial success than technical superiority or developing frontier models. - [07:31](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=451s) Building a high-margin AI agency relies on identifying and automating unglamorous, high-friction operational bottlenecks—such as drafting state-specific legal demand letters.

## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) AI Workers Building AI Workers: The Autonomous Agent Swarm

Joel Yi kicks off his presentation by urging the audience to close their laptops, get off Facebook, and pay close attention to a silent 60-second GIF playing on the screen. He narrates the demonstration in real time, explaining that he woke up that morning facing an overwhelming workload and decided to test a simple premise: having his autonomous AI agents spawn additional autonomous agents to handle the workload for him. Running on Hostinger, his Hermes agent is shown orchestrating the setup and deployment of five new functional agents from scratch without human intervention. By the end of the GIF, the system has spun up distinct roles, including an AI project manager and an AI funnel builder, ready to execute tasks directly inside his marketing agency.

Taking a quick pulse of the room, Joel asks for a show of hands to gauge familiarity with the open-source agent ecosystem, polling the audience on frameworks ranging from well-known to obscure: OpenClaw, Hermes agents, LangGraph, LangChain, and the Claude Agent SDK. The takeaway, he emphasizes, is that "AI workers can now build other AI workers." This dynamic points directly toward a phase of artificial general intelligence (AGI) characterized by self-learning and self-healing multi-agent swarms. Acknowledging that his talk’s title—"Stop Hiring: Build an AI Workforce Instead"—can sound provocative or anti-human, Joel insists that understanding this structural transition offers business owners an entirely different, highly leveraged blueprint for scaling.

```mermaid flowchart TD accTitle: Autonomous Agent Generation and Deployment Flow subgraph Orchestration["Agent Orchestration Engine"] A["Primary Hermes Agent"] --> B["Hostinger Infrastructure"] B --> C["Automated Provisioning"] end

subgraph DeployedSwarm["Deployed Autonomous Workforce"] C --> D["AI Project Manager"] C --> E["AI Funnel Builder"] C --> F["Specialized Sub-Agents"] end

D --> G["Agency Execution & Operations"] E --> G F --> G ```

[02:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=120s) From Handcoding Neural Networks to Deploying Agent Workforces

Joel grounds his perspective in his technical and operational background. Back in 2018—four years before ChatGPT revolutionized the industry—he handcoded a neural network from scratch that won first place in his university’s capstone competition against hundreds of students. The project, a plant and flower identification application, required six grueling months just to train on 10,000 images of roses. He jokes that the ordeal made him never want to look at a rose again, especially since debugging back then required hands-on intervention from his professors. In contrast, modern tools like ChatGPT and Claude Code allow developers to recreate that entire six-month project in less than twenty-four hours.

Following his academic work, Joel served as a U.S. Army cybersecurity officer, becoming one of the first one hundred personnel assessed into that specific branch after securing a Top Secret clearance at age twenty. Today, alongside giving TEDx talks and training students at MIT and the Harvard Kennedy School, he hosts international AI deployment workshops, including large-scale events in Seoul, South Korea. Outside of enterprise tech, he directs a non-profit foundation in his home country of Malaysia, where underserved youth are taught to build autonomous AI agents. The initiative helps participants secure technical roles earning three to four times their previous baseline wages, serving as a direct economic vehicle to combat human trafficking. In commercial operations, Joel’s company focuses specifically on building and operationalizing AI agents for small and medium-sized businesses (SMBs) rather than enterprise giants.

- Handcoded a flower-recognition neural network across six months using 10,000 training images in 2018; modern LLM coding environments reproduce the same outcome within a single day. - Commissioned as an early U.S. Army cyber officer with a Top Secret clearance obtained at age 20. - Operates a Malaysian non-profit training marginalized youth in agent development to triple or quadruple local income levels and prevent trafficking exploitation. - Specializes in building and implementing autonomous agent pipelines tailored specifically for SMB agencies rather than massive enterprise clients.

[03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) Anatomy of a Production AI Agent: 200 Franchise Locations on Autopilot

To prove that autonomous agents are practical revenue drivers rather than theoretical toys, Joel breaks down a live client implementation: an automated direct-message (DM) setter and receptionist deployed across a 200-location vehicle tinting franchise in Colorado. Operating inside GoHighLevel (GHL), the bot autonomously handles inbound inquiries, answers technical windshield and tinting specifications, calculates final price estimates, handles pricing drops, and manages the entire customer calendar by scheduling bays for specific dates and morning time slots. Crucially, the bot closes transactions directly: it follows up persistently until collecting a deposit, confirms receipt of funds, and connects via API to the automotive shop management software Shopmonkey to schedule technicians into service bays.

Joel pulls up the agent's core architecture, packaged in a single configuration file titled `SOUL.md`. The document demystifies agent development by breaking complex operational behavior into clean, plain-text rules: - An overarching behavioral persona dictating how the setter presents vehicle options and negotiates prices. - Rigorous automated follow-up sequences triggered whenever a prospect goes silent. - Objection handling matrices covering pricing hesitations, military discounts, and in-person cash deposits. - A deterministic human handoff protocol that escalates unresolved questions directly to human agency staff.

Every production agent, Joel explains, simply balances three fundamental components: the "Brain" (the underlying LLM), the "Hands and Arms" (the execution harness or tooling that takes action across external software), and the "Guardrails" (the left and right limits governing what the system can and cannot say or execute). He reassures the audience that building these tools requires no mystical skill set: "The people that will get replaced, it's not because of AI... The people that will replace the people that get replaced will be from people that actually can build AI agents."

```mermaid flowchart LR accTitle: Production DM Setter Integration Pipeline subgraph Inbound["Customer Interaction"] A["Customer DM"] --> B["GoHighLevel CRM"] end

subgraph AgentCore["SOUL.md Agent Harness"] B --> C["Brain: LLM Reasoning"] C --> D{"Confidence Check"} D -- "Standard Path" --> E["Objection / Pricing / Calendar"] D -- "Uncertain" --> F["Human Handoff Fallback"] end

subgraph Execution["Backend Action"] E --> G["Stripe / Deposit Collection"] G --> H["Shopmonkey Bay Scheduling"] end ```

[07:31](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=451s) Distribution Beats Tech: Finding and Monetizing $10,000 Problems

Shifting to commercial strategy, Joel challenges the audience's assumptions about technical superiority versus market execution. While many developers and computer science graduates may possess deeper raw coding abilities, technical excellence alone does not determine market success. "I will always beat you on one thing: I will always beat you on distribution and business outcome," Joel asserts. He describes the tragic archetype of the brilliant engineer who builds world-class systems that wither in obscurity: "Imagine being the best in the world at tech but nobody knows who you are. That’s like the worst demise in my mind."

The path to building a high-margin AI agency does not require competing with OpenAI or inventing the next frontier foundation model. Instead, founders should consult directly with SMBs and professional services firms to identify straightforward, unglamorous bottlenecks—what he terms the "$10,000 problem." As an example, Joel cites an engagement with a top-six U.S. law firm. During discovery, the firm revealed that paralegals and attorneys spent hours manually drafting state-specific demand letters. Rather than having attorneys navigate ChatGPT prompts, the firm paid Joel's team $15,000 to construct a dedicated, repeatable autonomous agent that drafts demand letters strictly adhering to distinct state laws.

Joel concludes by sharing a QR code granting free access to three developer training modules: - A step-by-step tutorial on building an initial OpenClaw agent. - A foundational guide for configuring and deploying Hermes agents. - A commercial framework for launching and scaling a forward agent deployment agency.

Directing attendees to his content via `@aiclawbots` on Instagram, he leaves the room with a final directive to stop overcomplicating the technology and start actively deploying agents against high-value real-world workflows.

## Notable quotes > [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) AI workers can now build other AI workers. — Joel Yi

> [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) Stop Hiring: Build an AI Workforce Instead — Joel Yi

> [03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) The people that will get replaced, it's not because of AI... The people that will replace the people that get replaced will be from people that actually can build AI agents. — Joel Yi

> [07:31](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=451s) I will always beat you on one thing: I will always beat you on distribution and business outcome. — Joel Yi

> [07:31](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=451s) Imagine being the best in the world at tech but nobody knows who you are. That’s like the worst demise in my mind. — Joel Yi

## Mentioned Joel Yi, Deploy AI Bots, Hostinger, Hermes Agent, OpenClaw, LangGraph, LangChain, Claude Agent SDK, Claude Code, ChatGPT, U.S. Army, MIT, Harvard Kennedy School, GoHighLevel, Shopmonkey, OpenAI, @aiclawbots

## Video description Joel Yi - Founder & CEO, Deploy AI Bots

After training 300+ companies on deploying their first AI Agents, I'll show you how AI is moving beyond chatbots into fully autonomous systems that can run real parts of your business. Most companies are still experimenting with tools, but I’ll explain how to deploy AI agents that handle operations, sales, and customer support end-to-end. You’ll learn how SMBs are already replacing repetitive workflows with intelligent systems like OpenClaw and Hermes Agents. I’ll break down practical use cases, costs, and how to get started without a technical team. If you want to scale faster with fewer people, this session will give you the blueprint.

[Source](https://www.youtube.com/watch?v=1eWQ666_vNQ)

中文简报

Deploy AI Bots 用自主智能体集群取代中小企业员工业务增长取决于针对具体的运营瓶颈部署自主智能体集群，而非构建前沿模型，这使得传统中小企业的招聘模式走向过时。 Joel Yi 提出了一个以自主 AI 智能体劳动力取代传统招聘的蓝图，开篇演示了主 Hermes 智能体在无需人工干预的情况下启动专用子智能体的过程。为说明生产可行性，他深入剖析了通过 GoHighLevel 和 Shopmonkey 在拥有 200 家门店的汽车贴膜连锁业务中运行的自动化私信跟进与预约接待系统。他解释称，稳健的生产级智能体依赖三大实用支柱：负责推理的“大脑”（Brain）、执行工具的“双手与双臂”（Hands and Arms），以及在诸如 `SOUL.md` 等简单配置文件中设定的行为“护栏”（Guardrails）。最后，Yi 敦促创始人将分发渠道和业务成果置于技术完美之上，并展示了瞄准起草法律催告函等不起眼却高价值的业务瓶颈，如何能够带来高利润的企业级代理合同。自主 AI Agent 已具备自我编排与繁衍能力，现场演示的 Hermes Agent 在无需人工干预的情况下，自动部署了涵盖项目经理与漏斗搭建师等 5 个专业子 Agent，标志着自愈多智能体集群向实用化 AGI 迈进。 (0:04) 软件研发周期发生断崖式压缩，2018 年需耗时 6 个月手写的神经网络项目如今用现代 LLM 工具在 24 小时内即可复现；同时，针对弱势青年开展的 Agent 工程培训不仅使学员收入提升 3 至 4 倍，也成为了防范人口贩卖的有效经济手段。 (2:00) 生产级 AI Agent 正端到端接管真实商业流程，例如科罗拉多州一家拥有 200 家门店的汽车贴膜连锁店通过 SOUL.md 规则文件与 API 集成，全自动完成了客户答疑、精准报价、定金收取与工位排期排班。 (3:45) AI 商业化的核心竞争力在于分发与业务成果而非单纯比拼技术深度；团队针对中小企业与专业机构挖掘高价值痛点，例如以 15,000 美元为全美排名前六的律所构建了自动起草各州定制索赔函的独立 Agent 系统。 (7:31)

Reading summary

Entrepreneur Joel Yi argues that businesses can scale more effectively by deploying autonomous AI agent swarms rather than expanding human headcounts, asserting that commercial success depends on solving specific operational bottlenecks rather than advancing core model technology. Self-Generating Agent Swarms and Infrastructure (0:04) Yi describes a shift toward multi-agent systems where autonomous tools generate and supervise subsequent task-specific bots. Demonstrating an implementation running on Hostinger, Yi showed a primary Hermes agent autonomously spinning up five functional agents—including a dedicated AI project manager and an AI marketing funnel builder—to absorb operational agency workloads without manual intervention. According to Yi, frameworks such as OpenClaw, Hermes, LangGraph, LangChain, and the Claude Agent SDK mark an initial phase toward artificial general intelligence driven by self-learning and self-healing agent swarms. He contends that this architecture presents an alternative scaling model for small and medium-sized businesses (SMBs), shifting the operational focus from managing human employees to orchestrating digital workforces. The Anatomy of Production AI Agents (3:45) To demonstrate production viability, Yi outlines a live deployment operating across a 200-location vehicle tinting franchise in Colorado. Built inside the GoHighLevel CRM, the automated direct-message receptionist handles customer inquiries, provides technical tinting specifications, calculates estimates, manages bay availability, collects deposits, and writes service appointments directly into the automotive management platform Shopmonkey via API. Yi explains that functional production agents rely on three modular elements: the 'Brain' (the foundation large language model), the 'Hands and Arms' (the tooling and execution harness connecting external software), and the 'Guardrails' (operational limits). In this deployment, these rules are governed by a plain-text configuration file titled SOUL.md, which details behavioral personas, multi-step follow-ups for non-responsive leads, objection-handling logic for military discounts or cash payments, and a deterministic fallback protocol to transfer complex queries to human staff. Prioritizing Distribution Over Technical Superiority (7:31) Yi argues that technical excellence alone does not determine market success, stating that business outcomes and distribution channels consistently outperform pure engineering capability. Rather than attempting to develop frontier foundation models in competition with enterprise AI labs, he advises founders to identify unglamorous, high-friction bottlenecks within professional services and SMBs, which he terms the '$10,000 problem.' As an illustration, Yi cites a client engagement with a top-six U.S. law firm where attorneys and paralegals spent extensive billable hours manually drafting state-specific demand letters. Rather than requiring legal staff to craft individual prompts, Yi's team built a dedicated autonomous agent adhering to specific state legal statutes for a $15,000 fee, resolving a repeatable business inefficiency through a targeted, automated pipeline. Compressing Development Cycles and Labor Impact (2:00) Grounded in his background as a former U.S. Army cyber officer, Yi contrasts modern agent generation with traditional software engineering. In 2018, building an award-winning plant-recognition neural network required him six months to train on 10,000 rose images, whereas modern LLM coding environments reproduce the same functionality in under 24 hours. Addressing workforce displacement, Yi claims that workers will not be replaced by AI itself, but rather by individuals who know how to build and operate agent pipelines. He links this model to his non-profit work in Malaysia, where training underserved youth to deploy autonomous agents reportedly increased baseline income by three to four times, positioning practical agent construction as an accessible, high-leverage technical skill. Yi concludes that the commercial value of AI agents lies in applying pragmatic, rule-bound workflows to concrete business problems rather than waiting for frontier breakthroughs, though his operational blueprints still rely on strict guardrails and human handoff protocols to mitigate agent failure.

精炼阅读摘要

演讲者Joel Yi主张，企业无需依赖传统的人力招聘模式，而是可以通过构建并部署自主AI智能体集群来实现高杠杆的业务扩张。他认为，随着AI具备自主配置与生成其他智能体的能力，聚焦业务分发渠道并解决具体的高价值流程瓶颈，远比单纯追求底层算法技术更具商业价值。智能体自生成机制与多代理集群演进 (0:04) Joel Yi在现场演示中展示了一个部署于Hostinger基础设施上的Hermes核心智能体。该系统在无需人工介入的情况下，自主编排并配置了五个具备独立职能的子智能体，包括AI项目经理与AI漏斗搭建师，从而在营销机构内部直接承接端到端的业务流程。Yi指出，这类技术涵盖了OpenClaw、Hermes智能体、LangGraph、LangChain以及Claude Agent SDK等开源与商用框架生态，标志着技术正在迈入“AI工人自主构建其他AI工人”的全新阶段。依据Yi的预测，多智能体之间的协同编排正推动技术体系向具备自我学习与自愈能力的通用人工智能（AGI）集群演进。尽管他承认“停止招聘，转而构建AI劳动力”这一主张可能引发关于劳动力替代的争议，但他强调，对于中小企业经营者而言，理解这种底层技术结构转型，可以摆脱线性人力雇佣的扩张成本，转而采用一种高杠杆率的数字劳动力扩展蓝图。开发范式跃迁与技术赋能实践 (2:00) Yi结合自身的工程经历说明了大语言模型带来的研发范式革新。2018年，他曾耗时六个月纯手工编写神经网络算法，利用10,000张玫瑰图像进行训练，并在高校毕业设计竞赛中斩获第一名；而如今借助ChatGPT与Claude Code等大语言模型开发工具，同等复杂度的应用在24小时之内即可复现。他曾担任美国陆军网络安全军官，并在20岁时取得绝密安全许可，这些背景使他意识到传统手动编写代码的技术门槛正在被新型工具体系迅速平民化。在商业与社会实践层面，Yi将重点放在中小企业服务与技术普惠上，而非服务超大型企业。他在母国马来西亚设立了非营利基金会，面向弱势青年提供自主智能体开发培训。该项目使学员获得了三至四倍于以往基线水平的收入，直接作为抵御人口贩运经济风险的手段。此外，他还通过在麻省理工学院、哈佛大学肯尼迪学院及韩国首尔举办的实操研讨会，向全球开发者推广中小企业智能体工作流的落地方法。生产级智能体的三层架构与业务落地 (3:45) 为证明智能体的实际营收能力，Yi以科罗拉多州一家拥有200家分店的汽车贴膜连锁企业为例，解析了基于GoHighLevel构建的自动化私信接待员案例。该智能体能够自主解答贴膜技术参数、计算阶梯报价并处理降价谈判，同时调取工位日程并向客户锁定服务时段；它还会持续跟进直至收取定金并验证款项，最终通过API对接汽车维修管理软件Shopmonkey以完成技师排班。 Yi展示了名为“SOUL.md”的纯文本配置文件，指出生产级智能体的开发本质在于规范化设定：涵盖设定行为角色、客户无应答时的自动化跟进序列、针对价格犹豫及军人折扣等场景的异议处理矩阵，以及在无法确信时转向人工客服的确定性转接协议。他将生产级智能体归纳为三大基础构件：“大脑”（底层LLM推理能力）、“手和臂”（跨外部软件的执行工具链）与“防护栏”（限制发言与执行权限的边界规则）。Yi由此断言，未来并非AI直接淘汰人类，而是掌握AI智能体构建能力的人将取代无法使用AI的人。分发优先策略与高价值业务痛点变现 (7:31) 在商业化路径上，Yi提出分发能力与商业成果交付比纯粹的技术深度更为重要。他警告技术人员不要陷入空有技术却缺乏市场认知度的困境，并指出AI代理机构无需尝试与OpenAI等巨头在基础模型层面竞争，而应主动深入各垂直行业，挖掘价值达到“一万美元”级别的业务痛点。作为具体佐证，Yi分享了其团队与一家全美排名前六的律师事务所的合作案例。该律所的律师与律师助理以往需花费大量时间手动起草符合各州特定法律要求的索赔函（demand letters）。Yi的团队并未要求律所员工自学提词技巧，而是以15,000美元的合同对价为该所定制了严格遵循各州法规的自动化索赔函生成智能体，有效解决了实际工时浪费问题。他总结称，将现有的智能体工具封装为解决具体垂直业务结果的交付方案，才是当前技术变现最高效的路径。 Joel Yi总结认为，创业者不应将技术过度复杂化，而应聚焦于利用开源框架和标准化提示词架构，迅速针对高价值的现实工作流部署自主智能体。不过，该演讲中的案例主要依赖规则化的配置文件与特定SaaS系统的API串联，对于跨多软件协同中的长期稳定性、异常容错机制以及涉及法律文本时的合规责任归属等深层工程限制，演讲中并未给出详尽的技术解决方案。

Detailed notes — zh

## Background 本内容由频道 Ai4 于 2026 年 8 月 18 日发布在 YouTube 上。页面简介显示，Deploy AI Bots 创始人兼 CEO Joel Yi 结合为 300 多家企业部署 AI 智能体的实战经验，分享了如何超越普通聊天机器人，利用 OpenClaw 和 Hermes 等智能体系统构建端到端自主运转的 AI 劳动力，帮助中小企业在不依赖庞大技术团队的情况下以更低成本快速规模化拓展业务。 ## TL;DR Joel Yi 开场展示了 Hermes 智能体全自动克隆并部署项目经理与漏斗搭建师等子智能体的过程，提出“停止雇佣，改为构建 AI 劳动力”的核心主张。他结合自身陆军网络安全背景与非营利实践，深入剖析了为拥有 200 多家加盟店的汽美连锁品牌打造的端到端 AI 销售接待员方案，涵盖报价、排期锁定、定金催收及工单下发全流程。随后，他拆解了底层结构清晰的 soul.md 配置文件以及智能体“大脑、手脚、护栏”三要素，强调技术开发者的核心壁垒在于分发能力与商业转化，并以律所 15,000 美元自动起草催告函为例，号召开发者深入真实场景解决具体高价值问题。 ## Atmosphere & style 整场演讲充满高频实战干货与极强的感染力，Joel 的表达自信且接地气，开篇用无声动图和开源生态调研迅速拉近与台下开发者的距离。从早年手敲神经网络认玫瑰花的幽默自嘲，到展现严肃的陆军经历与公益初心，情绪节奏起伏分明。现场兼具直击痛点的商业清醒与前沿技术的探索激情，既破除了听众对智能体落地的畏难情绪，又带来强烈的认知冲击与实操紧迫感。 ## Key takeaways - [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) AI 智能体已实现自主克隆与部署，核心智能体无需人工介入即可自动配置项目经理或销售漏斗搭建师等专门子角色，迈向自学习与自愈的蜂群协作阶段。 - [00:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=45s) 开源智能体生态正在快速演进，LangChain 与 LangGraph 已成为当前行业主流基础设施，OpenClaw 与 Hermes Agent 也在开发者中建立了广泛认知。 - [01:30](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=90s) Joel 提出“停止雇佣，改为构建 AI 劳动力”的核心论点，主张企业应打破将 AI 仅当作聊天机器人的局限，将其转变为接管真实业务流程的自主系统。 - [02:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=120s) 相较于过去耗费数月手工清洗数据训练神经网络，现代高级大模型开发工具已将同等复杂度的软件开发周期缩短至一天之内。 - [03:15](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=195s) 商业化定位应避开臃肿庞杂的超大企业（Enterprise），专注于为中小型企业（SMB）及营销代理机构提供快速落地且易见成效的智能体部署服务。 - [03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) 汽美连锁案例证明了 AI 销售接待员的全流程交付能力：实现车型匹配、方案报价、工位预约、定金催收，并无缝将工单写入 Shop Monkey 排班系统。 - [05:37](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=337s) 实战中的 AI 智能体本质上均可抽象为三大组件：提供推演逻辑的大脑（LLM）、执行系统操作的手脚（工具集成）以及设定安全边界的护栏（Guardrails）。 - [06:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=360s) 以 soul.md 为代表的结构化配置文件足以为智能体注入跟进规则、客户异议处理与付款政策，并配合毫秒级的人工无缝接管机制（Human Handoff）作为兜底。 - [07:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=420s) 未来取代平庸劳动者的不是 AI 技术本身，而是那些掌握动手构建并编排 AI 智能体能力的从业者。 - [07:30](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=450s) AI 应用层竞争的核心壁垒已从单纯的底层编码能力转向分发能力（Distribution）与商业结果的交付转化。 - [08:15](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=495s) 开发者不必执着于成为下一个 Sam Altman，而应深入传统业务的实际痛点，通过解决像“自动起草律所催告函”这类具体需求，赚取数万美元的高回报收益。 ## Detailed walkthrough ### [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) AI 智能体自主克隆与开源生态调研演讲者 Joel Yi 开场直接邀请台下的听众暂时放下手中的工作并退出社交软件，花整整一分钟观看屏幕上一段无声的演示动图。Joel 讲述了当天早晨的场景：面对堆积如山的工作，他萌生了一个念头——“如果能让我的 AI 智能体完全自主地批量创建更多智能体替我分担工作，那该有多好？” 动图里展示了他利用虚拟主机平台 Hostinger 与 Hermes Agent 的自动化过程。由一个 Hermes 核心智能体主导，在 Joel 完全不插手的情况下，屏幕上自动配置并部署了五个全新的子智能体。随后，这些智能体被直接输送到他的业务代理流程中。展示结尾清晰地列出了已经准备就绪的专门角色，例如负责流程管控的“AI 项目经理”（AI Project Manager）和专精转化链路的“AI 销售漏斗搭建师”（AI Funnel Builder）。在向全场证明“AI 员工如今能够亲手制造其他 AI 员工”之后，Joel 开展了一场针对开源智能体工具的现场举手调研，以此摸底观众的技术认知广度： - **OpenClaw**：现场有相当一部分人举手，知名度颇高。 - **Hermes Agent**：同样有较多开发者了解并举手回应。 - **LangGraph 与 LangChain**：台下反响极为热烈，属于当前行业中最主流的基础框架。 - **Claude Agent SDK**：举手的人寥寥无几，相对较为小众。 Joel 强调，这一技术路径标志着通用人工智能（AGI）正在迈向能够支持具备“自学习”（Self-learning）与“自愈能力”（Self-healing）的 AI 蜂群（AI swarms）阶段。因此，他本次演讲的核心主张正是“停止雇佣，改为构建一支 AI 劳动力”（Stop hiring, build an AI workforce instead）。尽管这个提议在当下往往会引发诸如“你是不是反人类、想要剥夺大家工作”的道德争议，但他希望在接下来的近八分钟里，从全新的商业视角切入展开拆解。 --- ### [02:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=120s) Joel Yi 的技术履历、公益事业与 SMB 定位 Joel 向大家分享了自己的背景。早在 2018 年，即 ChatGPT 问世整整四年前，他就凭借纯手工敲代码构建的神经网络模型，在几百名竞争对手中脱颖而出，夺得大学毕业设计第一名。当时他开发了一款用于识别植物花卉的软件，光是为了训练识别玫瑰花，就花了整整六个月时间导入并处理一万张照片。“从那天起，我发誓我再也不想看到玫瑰花了”，调试过程中哪怕遇到一个 bug，没有教授在旁边指导甚至都无法修复。反观当下，借助 ChatGPT 或 Claude Code 等先进工具，开发同等水平的软件甚至不需要一天时间。 Joel 还曾担任美国陆军网络安全官员（Cybersecurity Officer），由于早在 20 岁就获得了绝密安全许可（Top Secret Clearance），他成为了该兵种首批仅有的前一百名受聘入伍者之一。此外，他是一名 TEDx 演讲者，在全球多地（例如韩国首尔）举办大型线下实战训练营，专程教授 OpenClaw 和 Hermes 等开源智能体的落地部署。不久前的四月份，他还受邀登上哈佛大学肯尼迪学院（Harvard Kennedy School）讲台，并为麻省理工学院（MIT）及波士顿各大高校的学生开展前沿培训。在商业之外，Joel 提到了自己在祖国马来西亚运营的非营利机构。该组织致力于向当地资源匮乏的弱势青少年教授构建 AI 智能体的硬技能，帮助他们获得相比当下高出三至四倍的薪水，从而从根源上抵御人口贩运问题。回到商业业务层面，Joel 明确定位 Deploy AI Bots 并不针对臃肿的大企业（Enterprise），而是专注于为销售与营销代理商以及中小型企业（SMB）打造实用的智能体系统。 --- ### [03:45](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=225s) 汽美连锁店全自动 AI 销售接待员实例为展示智能体如何端到端交付商业成果，Joel 拿出了一套部署在客户 CRM 系统 GoHighLevel（GHL）上的“AI 私信转化员”（AI DM setter / Receptionist）真实对话记录。服务对象是一家位于科罗拉多州的车辆隔热膜与隐形车衣改装公司（Turbo Tinting），该品牌在全美拥有超过 200 家加盟门店。在这个复杂场景中，AI 销售智能体全面接管了门市销售与预约业务。 ```mermaid flowchart TD accTitle: 汽车贴膜 AI 销售接待员全流程系统 subgraph GHL["CRM (GoHighLevel) 智能体交互"] A["潜在客户私信咨询"] --> B["车辆知识库匹配 & 方案报价"] B --> C["意向跟进与异议解答 (智能降价促销)"] C --> D["选定工位档期 (如周四/周五早8点)"] D --> E["定金催收与在线收款确认"] end subgraph Ops["履约排班 & 人工托底"] E --> F["登录 Shop Monkey 自动为车间技师锁定工位"] C -. 出现未知异常超纲问题.-> G["Human Handoff 无缝转交人类客服接管"] end ``` 从对话实录中可以看到，AI 接待员展现出惊人的业务完整性： - **方案与报价**：熟练掌握各车型的挡风玻璃、后车窗及车身贴膜规格并给出最终报价。 - **排期引导**：智能建议并锁定到店安装日期与具体时间点（如周四或周五上午 8 点）。 - **促单收款与追款**：主动向顾客发送定金支付请求，若是客户迟迟未动，系统会自动按节奏多轮跟进提醒；客户完成付款后，智能体立刻给出明确的支付确认账单。 - **第三方工单下发**：智能体直接调用汽修汽美专用排班平台 Shop Monkey，自主将订单分配给特定工位的安装技师。凭借这样端到端的实战效果，企业客户每个月心甘情愿支付数万美元的代运营与部署费用。 --- ### [05:37](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=337s) 解析 soul.md 架构与核心壁垒转移为了破除大家对 AI Agent 的技术畏难情绪，Joel 展示了驱动整个接待员系统的核心配置文件——`soul.md`。虽然字号密集在投影上看不清每一个词，但其核心逻辑其实非常透明规整，并不高深复杂： - 顶部板块清晰定义了“多轮跟进规则”（Follow-ups）。 - 中间板块详细列举了“客户异议处理机制”（Objections handling），比如如何打消对贴膜隔热效果或价格的疑虑。 - 底部板块则写入了“定金支付指引、线下门店现金支付流程、退伍军人专属折扣（Military discount）政策”。 - 兜底环节配备了完善的“人类接管机制”（Human handoff）：只要遇到规则外的棘手问题，系统会自动在毫秒级将对话转交给团队成员或门店老板。 Joel 总结道，不论底层选用 LangGraph、Claude Code、OpenClaw 还是 Hermes，智能体本质上都可以抽象为三大组件： 1. **大脑（Brain）**：即底层大语言模型（LLM），负责语义理解与推演。 2. **手脚（Hands & Arms）**：负责实际执行系统交互的 Agent 框架与集成工具（Tool Harness）。 3. **护栏（Guardrails）**：设定行动的上下限与红线，规范“能说什么、能做什么、禁止越界什么”。正因如此，Joel 严肃指出了行业内的最大误区：**未来真正取代平庸劳动者的，不是 AI 本身，而是那些懂得亲自动手构建 AI 智能体的人。** 比起技术实现，Joel 强调未来真正的决定性壁垒是**分发能力（Distribution）与商业结果转化**。现场可能很多程序员的代码功力或产品能力都在他之上，但他断言自己总能在分发端获胜——他能做出现象级爆款视频，能吸引庞大流量。世界上最悲哀的事情莫过于“你拥有全天下最无敌的技术，却根本没有人知道你是谁”。在这个时代，开发者不一定非要去复制 OpenAI、成为下一个 Sam Altman。大家真正要做的，是深入到中小企业甚至是大型机构的真实泥潭里，挖掘并解决那些实实在在的“价值一万美元的问题”。 Joel 举了一个压轴案例：他们近期在为全美排名前六的大型律师事务所开发项目。在需求调研（Discovery call）之前，谁也想不到这帮顶级律所最迫切的痛点，居然仅仅是“根据不同州的法律条款自动起草律师催告函（Demand letters）”。在技术上，这只需调用 ChatGPT 并做好对应州的法律规则注入即可。然而，律所为了免去律师与律师助理每天数小时重复机械输入的痛苦，爽快地为这个智能体支付了 **15,000 美元** 的开发费用。最后，Joel 在大屏幕上打出二维码，为全场免费提供了价值上万美元的内部培训资料包，包含三大核心实战链接： - OpenClaw 智能体从零上手实操； - Hermes 智能体从零搭建与部署实操； - 如何启动并规模化运营一家面向前端的智能体部署代理公司（Forward Agent Deployment Agency）。 ### [09:56](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=596s) 总结与致谢：持续关注 AI Agent 落地实践 Joel Yi 在演讲的最后向现场观众做简短收尾。他带着标志性的热情与亲和力打趣道，大家显而易见能感受得到，自己平时围绕 AI Agent（人工智能智能体）制作了海量的深度内容（"I do a lot of content on AI agents as you can tell"）。他将自己在 Deploy AI Bots 协助数百家企业落地智能工作流的实战经验融入分享，并在结尾真诚地向参会观众致谢（"Thank you so much for your time, guys."），正式结束了这场关于用 AI 劳动力替代传统繁琐招聘的分享。 - Joel Yi 长期专注于 AI 智能体在企业端的落地应用与内容分享，倡导企业打破仅把 AI 当作聊天机器人的局限，迈向全面自治的智能工作流。 - 演讲在对与会者的感谢中圆满收尾。 ## Notable quotes > [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) 如果能让我的 AI 智能体完全自主地批量创建更多智能体替我分担工作，那该有多好？ — Joel Yi > [00:04](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=4s) 停止雇佣，改为构建一支 AI 劳动力 — Joel Yi > [02:00](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=120s) 从那天起，我发誓我再也不想看到玫瑰花了 — Joel Yi > [05:37](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=337s) 未来真正取代平庸劳动者的，不是 AI 本身，而是那些懂得亲自动手构建 AI 智能体的人。 — Joel Yi > [05:37](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=337s) 你拥有全天下最无敌的技术，却根本没有人知道你是谁 — Joel Yi > [05:37](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=337s) 价值一万美元的问题 — Joel Yi > [09:56](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=596s) I do a lot of content on AI agents as you can tell — Joel Yi > [09:56](https://www.youtube.com/watch?v=1eWQ666_vNQ&t=596s) Thank you so much for your time, guys. — Joel Yi ## Mentioned Joel Yi, Deploy AI Bots, Hostinger, Hermes Agent, OpenClaw, LangGraph, LangChain, Claude Agent SDK, Claude Code, ChatGPT, OpenAI, Sam Altman, GoHighLevel, Turbo Tinting, Shop Monkey, 哈佛大学肯尼迪学院, 麻省理工学院, 美国陆军 ## Video description Joel Yi - Founder & CEO, Deploy AI Bots After training 300+ companies on deploying their first AI Agents, I'll show you how AI is moving beyond chatbots into fully autonomous systems that can run real parts of your business. Most companies are still experimenting with tools, but I’ll explain how to deploy AI agents that handle operations, sales, and customer support end-to-end. You’ll learn how SMBs are already replacing repetitive workflows with intelligent systems like OpenClaw and Hermes Agents. I’ll break down practical use cases, costs, and how to get started without a technical team. If you want to scale faster with fewer people, this session will give you the blueprint. [Source](https://www.youtube.com/watch?v=1eWQ666_vNQ)

Source quotes

[0:04] Joel Yi: 如果能让我的 AI 智能体完全自主地批量创建更多智能体替我分担工作，那该有多好？ [0:04] Joel Yi: 停止雇佣，改为构建一支 AI 劳动力 [2:00] Joel Yi: 从那天起，我发誓我再也不想看到玫瑰花了 [5:37] Joel Yi: 未来真正取代平庸劳动者的，不是 AI 本身，而是那些懂得亲自动手构建 AI 智能体的人。 [5:37] Joel Yi: 你拥有全天下最无敌的技术，却根本没有人知道你是谁 [5:37] Joel Yi: 价值一万美元的问题 [9:56] Joel Yi: I do a lot of content on AI agents as you can tell [9:56] Joel Yi: Thank you so much for your time, guys.

Source quotes

[0:04] Joel Yi: AI workers can now build other AI workers. [0:04] Joel Yi: Stop Hiring: Build an AI Workforce Instead [3:45] Joel Yi: The people that will get replaced, it's not because of AI... The people that will replace the people that get replaced will be from people that actually can build AI agents. [7:31] Joel Yi: I will always beat you on one thing: I will always beat you on distribution and business outcome. [7:31] Joel Yi: Imagine being the best in the world at tech but nobody knows who you are. That’s like the worst demise in my mind.

Original transcript

[0:04] In the next uh 60 seconds, I want you guys to get off Facebook, get off your work. I want [0:08] you to watch a quick 60-second GIF. Uh and I'm going to talk through it because it's important. [0:12] There's no audio. So, here we go. I woke up this morning. I said, "Hey, I got a lot of work to do. [0:18] What if I could just have my AI agents create multiple more agents, 100% autonomous, so then [0:23] they can do more work." So this is a process here where we use hostinger uh and you guys heard of [0:29] Hermes agents. So my Hermes agent, that's right, is setting up five more as we speak without me [0:35] lifting a single finger. Then I can go deploy this into my agencies, right? So as you can see here, [0:40] we have five new agents 100% deployed, 100% working. In fact, you see at the end, [0:48] oh well, look that look at that. It's even showing you uh different things. But in fact, [0:52] you see at the end that we have multiple of them, the AI project manager, the AI funnel builder, [0:58] and they are ready to work and do great things in my agency. So, we're in this era right now, [1:04] and I want to do a quick survey. So, show of hands. I want to kind of test the room to see [1:08] what you guys know about open-source AI agents. Okay. From most popular to maybe obscure. So, [1:12] who here has heard of OpenClaw? Raise your hands. Okay. Yeah. Uh Hermes agents. Nice. Uh Langraph, [1:20] Lang Chain. Yeah, they're here. Uh, what about claude agent SDK? Fewer hands. Okay, so anyways, [1:29] u the process we're showing you today brings one point. AI workers can now build other AI workers. [1:36] And so we're getting into this phase where I think AGI is going to be strong enough where we can even [1:41] have proper AI swarms that can do self-learning and self-healing. So the title of my talk today [1:47] is stop hiring, build an AI workforce instead. And I realize it's a little bit controversial because [1:51] this is where people say, well, you know, do you not like humanity, right? You're getting me a job. [1:55] So, I have a very different perspective and spin. And I hope you guys will really enjoy the next [2:00] 7 minutes and 51 seconds. Cool. You guys good? Awesome. Look, my name is Joey Ee. Uh, I won in [2:06] 2018, four years before Chat GPT, I handcoded from scratch a neural network that won the first place [2:12] capstone in my university. So, I beat out a couple hundred students and I hadn't coded from scratch a [2:17] plant flower identification software. It took me six months to train the app on 10,000 photos of a [2:22] rose. That was the day I wanted to stop looking at roses because it was insane. I couldn't even [2:28] fix my code without my professor's help. But today, with Chad GPT cloud code, you could you [2:32] could build my app in less than a day. It's crazy. I was a former US Army cyber security officer and [2:38] uh one of the first hundred ever assessed into that branch. It was because I had my top secret [2:43] clearance at 20 years old. So pretty young to have a TS clearance. I'm a TEDex speaker. TEDx speaker. [2:49] I run the largest live events all around the world at Soul, South Korea on the far right. We [2:54] teach people how to deploy AI agents like Hermes and Open Claw. And I spoke at the Harvard Kennedy [2:59] School uh to the students and I did a training for MIT and top Boston colleges about a few months ago [3:04] in April. So they called me up to Harvard. Anyways, um I one last thing about me then [3:09] we'll continue. I also own a nonprofit in my home country, Malaysia. Anybody been to Malaysia? Oh, [3:14] okay. Cool. You guys heard of Malaysia? So, we teach underserved youth how to build AI agents [3:20] and give them jobs that pay them three to four times their current income today. And we fight [3:24] trafficing with that. So, that's a bit about me and my passion projects. Some of the brains [3:28] uh some of the brands rather we've built and trained bots for. We focus to marketing and sales [3:34] agencies. Currently, we're not enterprise. We're building AI agents for SMBs. Cool. Anyone here an [3:40] SMB? Yeah. Not enterprise. I know there's a lot more enterprise people, right? Cool. Okay. Now, [3:45] here's one of our bots and I want to show this to you to make a point. This is our AI [3:50] receptionist. It's an AI DM setter. This is a CRM called goal high level. Anybody know GHL? Okay, [3:56] some of you are familiar. It's a CRM. And you can see here our bot is clearly doing follow-ups and [4:02] even dropping the price. So, to give you context, this is a turbo tinting company in Colorado. They [4:07] sell tints for vehicles like windshields, right? Rear windshields. They wrap it, make it nice, [4:11] blocking the sun. So, our AI agent is fully running sales for this business. And that's [4:17] over 200 locations that this franchise owned. So, our bot knows vehicle knowledge and final pricing. [4:23] Our bot can help the customer pick a date to book their appointment for tinting. And the [4:28] bot can also select the time, 8 a.m., Thursday, Friday. I'm not sure if you guys can see that, [4:33] but that's kind of what it's showing here. Um, and lastly, the bot also collects money. That's [4:39] right. The bot also sells. And if they don't collect money, it's going to continue following [4:43] up until the person pays the deposit to get their car tinted. Um, yeah. So, like I said, follow up. [4:49] And you can see in the bottom slide, there's a payment confirmation, which is pretty cool, right? [4:52] It's like, "Hey, did you make this payment yet to book in your deposit?" And it's like, "Yeah, [4:56] awesome." Now, one more thing. The bot actually goes into this platform called Shop Monkey where [5:02] these tint companies schedule their tints and it books in the appointment for the technician in [5:09] the bay. So again, AI agents are incredible. They can do so much. We've established this [5:14] as a business use case where we're getting paid tens of thousands of dollars a month to do this, [5:18] right? But what is all this about and how does this affect you? Because clearly this talk is [5:24] really for you for the benefit of you. If you stay to the end and I'll leave, I'm going to give you [5:28] guys three free trainings that I teach all my students and devs that people have paid us tens [5:32] of thousands for. You guys can scan a QR code and download all that for free. Cool. Yes. All right. [5:37] I want to show you all that to actually show you it's soul.md. It's basically the file that [5:43] makes it up. Now, you can't read it, but I just want to show you that guys, friends, building AI [5:48] agents is not that complicated, right? There's the rules. That's the um what the AI agent does. The [5:55] DM setterbot, how it sells the tint. You can even see here, friends. Look, look at that top section, [6:00] follow-ups. That's how you do follow-ups. Middle section, how you handle objections, [6:04] right? We've learned this in sales, how to handle objections. Look at the bottom. Deposit, pay in [6:09] person, military discount. Any vets here like me? Thank you for your service, guys. But yeah, [6:14] it's not that complicated. And I think a lot of people are afraid or maybe they just haven't tried [6:19] building putting their hands on building an AI agent. I want you to walk away from my talk today [6:24] to realize like, hey, I need to go get my hands dirty and just build because that's the skill and [6:29] that's the point of my talk, guys. The people that will get replaced, it's not because of AI, right? [6:34] AI is not going to replace them. The people that will replace the people that get replaced will be [6:38] from people that actually can build AI agents. So, if you want to not be replaced, you have the skill [6:43] of building AI agents. You're going to be far ahead of your competitors and your peers. Okay. [6:47] And lastly, this is the um I can't even see my own oh the handoff, the human handoff. If the [6:54] bot doesn't know what to do, the bot will hand off to one of our team members or the clients, [6:58] right, to respond and boom, that's how the DMB bot works and that's what makes our customers [7:03] money and in return makes our agency revenue. So this is like the very simple explanation of any AI [7:10] agent. I don't care if you build it in Langraph, clot code, openclaw, Hermes, they're all the same, [7:15] right? You've got the brain, which is the LLM itself, the large language model, the hands, the [7:21] arms that do stuff, right? That's like the actual Asian harness itself, right? And then lastly, [7:26] guard rails, left and right limits, what you can or can't do, what you can or can't say. But in the [7:31] final two minutes here, what I really want to talk about because this is pretty common knowledge, [7:36] not talked about, is really how do you think about distribution? Because this is my fundamental [7:41] belief, friends. I don't care how good you are at tech. In fact, some of you guys here are probably [7:45] better than me at tech. Even though I have a comai degree, you might be better than me at [7:49] building products, right? You might have a better dev team. But one thing I can tell you is that I [7:52] will always beat you on one thing. I will always beat you on distribution and business outcome. [7:58] So I can distribute, I can go viral, my videos can go viral, and you can beat me on that. And I [8:03] think what sets us apart as business owners in the future is your skill to solve business problems [8:09] for entrepreneurs or to have massive distribution. And I think this the sad analogy is like imagine [8:16] being the best in the world at tech but nobody knows who you are. That's like the worst demise [8:21] in my mind. So my challenge to you guys is get really good at distribution and get really good [8:27] at learning how to solve the next 10,000 problem $10,000 problem. You guys, we don't need to be a [8:32] chat GPD. We don't have to be Sam Alman, right? We don't have to be any of these big people that [8:37] we look up to. All we have to do is talk to SMBs, even talk to enterprise, and figure out [8:43] the pain points they have. I'll give you a very simple example. Uh, in my last 60 seconds, we're [8:47] building for a law firm right now, a top six law firm in America. You know what's the most simple [8:52] use case they have that we didn't even know until we did a discovery call? They wanted an AI agent [8:57] that could draft demand letters by state. You guys know we can do that in chat GPT, right? Drafting [9:05] a demand letter with different state laws is not a hard bot to build. Somebody guess how much they [9:10] paid us to build that bot. $15,000. It's solving a problem that takes their lawyers parillegals hours [9:19] and hours to do. They don't want to use GPT and type it. They just want to bot that does it. So, [9:23] we just built that. That's what I mean by you need to find that $10,000 problem and solve it. [9:28] All right. So, friends, you've been awesome. If you want the free trainings like I talked about, [9:32] I'm right on time. 20 seconds, scan that code. Um, there's a PDF, three links in there, [9:37] completely free trainings. One is on how to build your OpenCloud bot for the first time. One is on [9:41] how to build your Hermes agent for the first time. And then the last one is if you want to build and [9:46] scale a forward agent deployment business. One of the previous speakers talked about that. Or want [9:50] to build your agency. We also have a training on that. That's it. Uh, I'm AI Clawbots on Instagram. [9:56] I do a lot of content on AI agents as you can tell. Thank you so much for your time, guys.