有观察者列出17条AI智能体趋势:Hermes智能体每完成任务后写入自身内存,本地模型让产品完全跑在用户设备上、无需接触数据,Agent成本正取代人力成本,企业或将50%以上人力预算用于购买tokens。此外,约12个月后可能出现观察用户一周即可无指令代其工作的智能体,YAML配置文件正取代组织架构图,而面向老年人的智能辅助系统是发展最滞后的领域。
转自GG老哥👇
以下是更多关于这些AI智能体的观察结果(我还会不断补充这个列表):
1. Hermes智能体在完成每项任务后都会将相关信息存储在自己的内存中。
这意味着,如果你现在就开始使用这些智能体,与6个月后才开始使用相比,你会获得明显的优势。
2. 我们可能再过12个月左右,就能拥有这样的智能体:它们能够观察你一周的工作过程,然后在没有任何指令的情况下自行完成你的工作。
目前,通过屏幕录制、智能体的记忆功能以及本地模型的组合,这一目标已经变得可行了。
3. 对于创业者来说,本地模型的重要性在于:你可以推出一种完全在用户设备上运行AI技术的产品,而无需接触用户的任何数据。
这样一来,就完全没有隐私风险、服务器成本,也不用担心合规问题。
这种技术会瞬间改变你可以进入哪些行业进行销售——医疗、法律、金融等所有那些不允许将数据传输到云端的领域,都会因此变得可行。
4. 在这些智能体真正发挥作用之前,每家公司都需要将自己重新打造成一个“第二大脑”——即一个能够辅助人类进行工作的智能系统。
这意味着:每一个流程、每一个决策,以及每一项机构知识,都必须以Agent能够理解的形式存在(即必须以Agent能够读取的格式进行表达)。
然而,大多数公司在这方面都做得非常糟糕(即这些信息并没有以代理能够理解的形式被保存或使用)。
5. 代理的薪酬成本实际上已经取代了传统的人力成本;
对于许多公司来说,将总人力资源成本的 50% 以上用于购买代币(tokens)也并不算什么疯狂的决定。
6. 代理们在无意中在企业内部引发了竞争:营销人员和销售人员虽然都在为不同的目标而努力,但他们实际上是在相互抵触、互相妨碍对方的工作效率。
而人类花了数十年时间才建立起跨部门之间的协作机制。对于这些“代理”来说,这个问题却从未被真正考虑到过。
7. YAML 配置文件正在逐渐取代传统的组织结构图:谁向谁汇报、他们拥有哪些权限、可以使用哪些工具——所有这些信息都通过 YAML 配置文件来定义。
公司的整体结构实际上就是一个可以被版本控制、分叉(fork)并部署的文件罢了……这确实是一个全新的模式。
8. 那些第一批能够识别出骗局的公司,将会因此获得巨大的价值(甚至可能价值数十亿美元)。
目前,许多代理人会毫不犹豫地将资金转给那些格式看似合法的虚假发票;他们的判断完全缺乏任何信任机制或理性分析。
9. 实际上,很多所谓的“专业知识”不过只是对某些信息的记忆罢了——比如税法规定、判例法内容、以及不同供应商的收费标准等。
只有当代理人能够将这些信息放在具体情境中加以理解时,他们的价值才会真正体现出来(即他们能够判断出哪些信息才是真正重要的)。具备这种能力的人其实非常少。
10. 我们都在使用相同的模型。
真正的区别在于我们为这些模型提供了什么样的输入数据。两位创始人即便使用相同的代理人、相同的模型和相同的工具,最终得到的结果也可能大相径庭——这完全取决于他们所掌握的知识质量。
如果输入的信息质量低劣,那么输出的结果自然也会很糟糕(这种情况会永远持续下去)。
11. 目前人工智能领域发展最为滞后的领域就是为老年人服务的智能辅助系统;
有约7000万的婴儿潮一代需要帮助填写医疗表格、处理保险索赔以及安排预约等事务。
12. 代理人的响应速度(即处理任务的速度),已经相当于网页的加载速度了。
如果你的客服代表需要 45 秒才能回复客户,那么客户很可能已经转而使用那些响应速度更快(仅需 13 秒)的客服服务了。
13. “技能文件”(Skills files)实际上就是新的“应用程序”;
一个能够指导客服代表如何高效完成某项任务的文档(如 SKILL.md),比那些通过登录界面来提供相同功能的 SaaS 服务更有价值。
14. 在人工智能硬件领域,如何开发出既实用又受消费者欢迎的产品呢?
其实只需要一个价格约为 30 美元的插件,就能将人工智能功能添加到现有的普通设备中。
比如,智能烤面包机并不需要从头开始设计;只需将这个插件连接到价值 15 美元的普通烤面包机上即可。
15. 客服代表的阅读速度远超人类的思考速度;
目前,每个客服工作流程中的瓶颈都在于人工审批环节——人类本身才是效率最低的部分。这确实是个令人反思的现象。
16. 客服代表让“80/20 规则”(即工作中 80% 的任务由人工完成、20% 的任务由自动化系统完成)变得更加明显。
如今,只有那 20% 的关键任务仍由人类负责,而剩下的 80% 的工作内容早已被自动化系统取代了。
许多原有的工作描述其实都隐藏在那些“被自动化处理”的任务中。
17. 我一直反复强调的一点是:如今最成功的商业模式,都是由那些比他们的客户稍微领先一点的人创造的——这种领先幅度既不是十年,也不是六个月。
这样的领先程度既能确保他们能够引领市场的发展,同时又足够接近客户的实际需求,使他们能够被客户真正理解。
More AI agent observations below (I keep adding to the list): 1. Hermes agents write to their own memory after every task. Which means starting today versus starting in 6 months is an unfair advantage for you. 2. We're maybe 12 months from an agent that can watch you work for a week and then do your job without any instructions. The screen recording plus agent memory plus local model combination makes this possible right now 3. The real reason local models matter for founders: you can ship a product where the AI runs entirely on the customer's device and you never touch their data. Zero privacy concerns. Zero server costs. Zero compliance headaches. That changes which industries you can sell to overnight. Healthcare, legal, finance, all the regulated verticals that won't send data to the cloud just opened up. 4. Every company needs to be rebuilt as a "second brain" before agents can be useful. That means every process, every decision, every piece of institutional knowledge has to exist in a format an agent can read. Most companies have none of this. 5. Agent costs are the new headcount. Won't be crazy for companies to spend 50%+ of their total headcount cost on tokens. 6. Agents are accidentally creating internal competition at companies. The marketing agent and the sales agent are optimizing for different metrics and working against each other without anyone realizing it. It took humans decades to develop cross-functional alignment. Nobody thought about it for agents. 7. The YAML config file is becoming the new org chart. Who reports to who, what permissions they have, what tools they access, all defined in a config file. The company's structure is literally a file you can version control, fork, and deploy. That's new. 8. The first agents that can smell a scam are going to be worth billions. Right now agents will happily wire money to a fake invoice because it matched the format. The trust layer is completely missing. 9. We're about to find out that most "expertise" was actually just memory. Knowing the tax code. Knowing the case law. Knowing which supplier charges what. When an agent holds all of that in context, the expert's value shifts from "I know things" to "I know which things matter." Much smaller group of people. 10. We're all running the same models. The differentiation is in what you feed them. Two founders with the same agent, same model, same tools will get wildly different results based purely on the quality of their knowledge base. Garbage context in, garbage output out. Forever. 11. The most underbuilt category in AI right now: agents for old people. 70 million boomers who need help with medical forms, insurance claims, and appointment scheduling. 12. Agent latency is the new page load speed. If your agent takes 45 seconds to respond, your customer already switched to one that takes 13. Skills files are the new apps. A SKILL.md that tells an agent how to do one thing well is more valuable than a SaaS subscription that does the same thing behind a login screen. 14. AI hardware... how do you create devices that are good businesses that people want? It'll be a $30 dongle you plug into existing dumb devices to give them an agent brain. Smart toaster doesn't need to be built from scratch. It needs a $30 brain attached to a $15 toaster. 15. Your agent can read faster than you can think. The bottleneck in every agent workflow is now the human approval step. We're the slow part. That's a strange thing to sit with. 16. Agents made the 80/20 rule violent. The 20% of work that matters is now the only work humans do. The 80% just disappeared. Entire job descriptions were hiding inside that 80%. 17. The thing I keep coming back to: the best businesses right now are being built by people who are just slightly ahead of their customers. Not 10 years ahead. 6 months ahead. That's the sweet spot. Far enough to lead. Close enough to be understood.在 X 查看被引用的帖子
来源:@berryxia · x.com