Proprietary Models
topic on 5 shows · 7 statements across 6 episodes
BG2 Pod 
Latent Space 
No Priors 
the a16z Podcast 
All-In
7 statements about Proprietary Models, every show
Moe: Open-weight inference can hit 500 tokens/sec, 2-3x faster than proprietary APIs
“But for open weight, when you are running it, every provider can offer potentially even 10 different levels of speed going from like the slowest mode, which can be a lot cheaper to 400 tokens per second almost up to 500 in many cases for some workloads. And th…”
Swix: Open-source token generation share will rise but stay well below 50%
“I think it's going to go up because of the amount of enterprise adoption of open models that I'm seeing.
And also there's a lot of demand.
Like there's the enterprises would much rather be on open models if they actually could get the performance they're loo…”
Brin admits DeepSeek's release closed the gap with proprietary AI models
“Deep Seek released a really surprisingly powerful model when it was January or so. So that, that definitely closed the gap to proprietary models.”
Dohmke: Open-source AI innovation equals proprietary model innovation
“As much as there's innovation on proprietary models and software, there is as equal amount of innovation in open source.”
Gurley: Startups Switch to Multi-Model Setups in Production for Cost
“When I look at what I see going on in the startup world, they might start with one of these, you know, really well-known service models that's proprietary, but the minute they start thinking about production, they become very cost focused and on the inference …”
Mensch: Open-source AI trails proprietary models by six months
“So really we think that the gap is closing. The gap is approximately six months at that point.”