One of the reasons we’re focused on training efficiencies is because those are upstream of end user costs. We achieved these specific gains while cutting context windows down 4x. That will have major implications for inference speed and expense.

For us specifically, we want to target prosumers who aren’t gonna pay $1 for a few seconds of footage. It has to be 1/50th of the cost of the big guys. It helps that our ultimate focus/niche is animation, so we can get away with smaller models.

But to answer question more broadly, I think you have to ask the questions:

1) How will this lab front run the hyperscalers? Is there someone thing they’re doing (or if executed correctly) could break right such that they could have a hook for customers to use the product over hypscaler (eg crazy low cost of inference)?

2) If this lab lands the hook and gets small lead how could they maintain and grow it? This doesn’t have to be technical per say. It could be a clear land and expand sales motion at the enterprise level, for example.

Startup history is littered with examples of companies that seemed too close to a big guy to sprout up in the first place (eg stripes adjacency to PayPal).

From the outside looking in it might seem likes there no space for the lab to bloom; so you’ll have to bring the specifics of the lab and your on the ground expertise of the space / company’s situation to come to an answer for yourself.