@monteluna
The term startup is overloaded now with companies who believe they can get rapid exponential growth, even though if you think about their business model, they usually have physical constraints or a non-sticky product. They mathematically cannot achieve hyper growth.
I feel as though the term startup should only be reserved for companies where:
1. The unit economics show costs are at least sub-linear as users increase
2. The product improves in quality as the users increase, making the product more sticky.
The current problem with AI is software and data businesses have a new cost which completely breaks the unit economics, and the balance sheet shows massive costs in energy before a single user shows up in the funnel. The break-even costs rise because the development means you spend more than $20-50 per user just to run and maintain the product.
This essentially makes these businesses *not* startups. Unless you have a ton of money tucked away for marketing to do appropriate lead generation, every spent token only *increases* the net cost per user. If the products use-case is directly AI for users, you can have certain users with insane token use and not a lot of revenue to show for it.
Whats worrying is young tech entrepreneurs and developers are falling into a trap. Most believe just making the product and praying for a VC to airdrop them money is going to work. If I can do this math, they can too, and they probably see these businesses as uninvestible as I do. Integrating AI is probably a *bad* thing for your business unless you have a clear reason to do so and can bound costs or have flexible pricing.
No one is going to invest in a business with weird unbounded, growing costs and a revenue model that doesn't grow with it. If I were in the VC seat, I wouldn't touch any of this stuff. The business is compute heavy and mostly is like a low margin, opex heavy, costly unit economics company.
Source: I worked for a ML company and the model is exactly the same (basically I made it up).