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.

Here are a couple of ideas I saw recently:

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.

    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.

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.

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.

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.

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.

Am I missing something important? That's why I'm asking here.

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.