花费数万亿美元仅仅是为了改善客户服务？
我看到了一些文章讨论AI公司赚钱的更大机会。结果发现，如果我是创始人或运营者，基本上有六种不同的赚钱方式。我不确定风险投资界的共识是否完全滞后于实际情况，所以我才提出这个问题。
以下是我最近看到的几个想法：
1. **AI收购**：这是一个非常热门，有时被过度炒作的话题。它涉及收购那些与AI整合程度较低但急需人力服务的公司。例子包括小城镇的会计事务所、IT托管服务（有时外包）、法律服务（不一定是顶尖律师事务所，而是帮助现有客户的地方性事务所）以及保险。之所以这个话题引起关注，是因为有人可能会发现收购和精简公司比出售软件更具优势。如今的软件可能需要重新定位以建立护城河，而许多可以自动化的任务集中在服务行业，尤其是在价值链的低端。风险投资公司也在寻找新的资产，因为传统的SaaS模型不再提供高回报。
表面上，这似乎是有道理的。但我想，如果是这样，那我们为什么还需要风险投资？风险投资公司的存在似乎有些滞后于当前的情况，而且商业模式可能并不那么有效。风险投资的最佳时期是在移动和云计算时代。
2. **AI自动驾驶/原生AI服务公司**：这涉及到开发AI自动驾驶技术，大家都知道服务即软件，或者建立原生AI服务公司。公司正在关注保险经纪、会计或税务审计等领域，基于“行动系统”构建公司。这意味着整合SAP、Salesforce或ServiceNow等产品，以便用户不需要使用20个不同的页面来管理采购、入职、期末结算、工单升级等。
3. **公司大脑**：这条路径涉及将Slack、电子邮件、工单、会议和数据库整合成一个可以成为我们公司大脑的代理。这可能是组织重组的一种方式，因为代理将更好地理解公司。
4. **可验证的工作**：大家都知道在做可验证工作的公司，而编码是第一个用例。但自2024年我第一次尝试使用Cursor以来，我还没有看到其他用例像编码那样流行。这让我觉得公司和投资者正在努力寻找下一个编码用例，但我们还没有找到。我们看到在合同红线、支持解决方案、质量保证或IT事件摘要等领域的尝试，许多公司已经在这些方面开展工作。
我的问题是，自2022年以来投资于AI的数万亿美元是否旨在改善效率和节省成本这一更大主题？我知道公司有很多问题需要解决，但如果这是最大的用例，风险投资的回报在哪里？在我看来，许多事情可以由私募股权公司来完成。增长股权或私募股权公司可以利用杠杆收购并投资于这些用例。私募股权公司可以利用其投资组合公司收购大量旨在简化工作流程的AI企业。与目前被炒作的估值相比，回报可能会慢得多，也许3倍或4倍的回报将是非常好的消息。
我是否遗漏了什么重要的内容？这就是我在这里提问的原因。
顺便说一下，我不是专业人士，也不住在湾区；我目前在上海，所以可能有我没有掌握的信息。谢谢。
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I came across a couple of articles discussing the bigger opportunities for AI companies to make money. It turns out there are pretty much six different ways to make money if I'm a founder or an operator. I'm not sure if the consensus from the venture capital world is entirely lagging behind what's actually going on, so that's why I'm asking this question.<p>Here are a couple of ideas I saw recently:<p>1. AI Roll-ups: This is a very hot, sometimes overhyped topic. It involves buying companies that are less integrated into AI but heavily need human services. Examples include accounting firms in small towns, IT managed services (sometimes outsourced), legal services (not necessarily top law firms, but local ones helping existing customers), and insurance. The reason this has gone viral is that some might figure out that buying and streamlining companies can be a better choice than selling software. Software nowadays might need to redirect to build its moat, and many tasks that can be automated surround the service sector, especially at the lower end of the value chain. Venture capital companies are also looking for new assets because traditional SaaS models no longer present high ROI.<p><pre><code> On the surface, this makes sense. But then I thought, if that's the case, why do we need venture capital? The existence of venture capital businesses seems a bit behind what's currently going on, and the business model may not work that well. The best times for venture capital were during the mobile and cloud eras.
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2. AI Autopilot / AI-Native Service Companies: This involves working on AI autopilot, where everyone knows service as software, or building AI-native service companies. Companies are looking into areas like insurance brokerages, accounting, or tax audits, building companies based on a "system of action." This means integrating products like SAP, Salesforce, or ServiceNow so users don't need to use 20 different pages to manage procurement, onboarding, period closing, ticket escalation, etc.<p>3. Company Brains: This path involves integrating Slack, email, tickets, meetings, and databases together into an agent that can become our company's brain. This might be a way for organizations to restructure themselves, as agents would understand companies much better.<p>4. Verifiable Work: Everyone knows about working on companies that do verifiable work, and coding was the first use case. But since 2024, when I first tried using Cursor, I haven't seen another use case as viral as coding. This makes me think that companies and investors are trying to figure out the next coding use case, but we haven't found it yet. We see attempts in areas like contract red lines, support resolutions, QAs, or IT incident summaries, and companies are already working on these.<p>My question is, can we say the trillions of dollars invested into AI since 2022 are aimed at the bigger topic of improving efficiency and saving costs? I know companies have many problems to solve, but if this is the biggest use case, where is the venture-scale return? From my perspective, many of these things can be done by private equity companies. A growth equity or private equity firm could use leveraged buyouts and invest in these use cases. A private equity company could use its portfolio companies to acquire a large number of these AI businesses that aim to streamline workflows. The returns, compared to currently hyped valuations, might be much slower, perhaps 3x or 4x would be very good news.<p>Am I missing something important? That's why I'm asking here.<p>By the way, I'm not a professional and I don't live in the Bay Area; I'm currently based in Shanghai, so there might be information I haven't grasped. Thank you.