https://www.yahoo.com/entertainment/tv/articles/televised-ne...
I think OpenAI actually will ultimately start a fleet of consultancy/SaaS spinoff companies, each perhaps short-lived until the core product does the job out of the box.
As for the cut, I am sure they will try. Where I work we have had a few projects that were built entirely with models - not a single line written or read by humans. One of them is now the fastest growing money maker for my company, reaching 89M of revenue on about 9% margin in 2 months of its existence, and growing.
It cost roughly 40k in tokens to build, 7 weeks from the first prompt to first production revenue, and was built and is being operated by 1.5 guys. Its a fantastic bang for buck for us, as previously such complex automations would have been impossible to build economically.
There will be an attempt to get a deeper cut of that value. We already see this, for instance, in Meta charging by message on Whatsapp from Oct 1, raising the costs for AI-powered chatbots delivered to consumers via whatsapp by 10-15x easily. More to come for sure.
If you really did it with 1.5 people and it is profitable, you will have 1000 clones in 1 year. The only moats will be first mover advantage (slim), connections (all right) and the possible secrecy of how much you're making (very slim).
Edit: I'd also argue its not as simple as vibing a trivial solution. It took deep domain knowledge, which is not in the public domain, and is built on decades of operating this sort of thing manually, and integrating it with dozens of external systems that help make this into a moneymaker. I am not saying its impossible to replicate, but it's not something one can replicate by merely watching it function.
I see a lot of fluffy things, which are automating the simplest part of a process, leaving a lot for the humans to deal with. This is the thing that can be cloned a 1,000 times in 2 weeks.
I'll give an example. In the system I mentioned, people are scheduled for paid procedures. The procedure takes 2 days, but often we have no-shows on the 2nd day. This 2nd day can be repurposed for someone who was a no-show before - its already paid for, and every penny counts. So the chatbots chase people who may be able to make the appointment last minute, and decrease the costs to operate. There are multiple people in such pools, so the system needs to decide whom to approach, how much time to wait for a decision, and so on. Its gnarly, hard stuff to do right.
The system I mentioned has about 150 such cases, and previously just building the automation would be too expensive, and maintaining it -- too slow. Its a living organism, with thousands of people using the system every day, and more edge cases emerging constantly. It is thanks to AI coding that the thing was economical to build, and its thanks to AI that we can iterate quickly enough to keep it working well. We get all the value, and the AI labs selling us tokens got a pittance.
In my industry, the automation of such processes was always a stated goal - for at least a decade, but its the first implementation that actually works end to end, is not hated by the end users, and does not require a lot of manual interventions. It was still very hard, but at least coding wasn't part of the problem.