@pHequals7 lol wtf it's way too early for that. i don't care the model is open sourced if it's shit. i can release an open sourced model by just finetuning some chinese model (which alex's team did previously with muse spark), it doesn't mean i'm worth $14b...
@piyush100x you need to understand that not every attention is good for your startup. it might be when you're running B2C, but for B2B definitely not. so i suggest pivoting to B2C before ragebaiting
smart people only need a well defined problem to solve. this is the 2nd most frequent way the most innovation happens. the most frequent by far is still pure luck (pre-conditioned by intelligence and perseverance)
fable still didn't solve thinking from first principles. it is like a junior employee with infinite knowledge - it can do whatever you tell it to do, but not really be creative
they're not going to b2b with this since there is no ZDR for fable, unlike other models. i genuinely don't know what's the market for this - too expensive for individuals, too non-compliant for companies. i hope they just end up keeping it in the sub and this was the marketing trick
@matt_slotnick@atelicinvest that would make sense if there wasn't a person with zero knowledge of ML leading their AI division. what's in fact going to happen is eternal catchup on benchmarks and no (even close) catchup on actual capabilities
let's say you're a user for 5 years. spotify has in this timespan more than doubled their user count from 350 million to 750 million. just building a system which can take on 400 million extra users is a huge effort, not to mention fighting constant changes in dependencies, security issues (which exploded recently due to AI), etc.
Perplexity has built their own models as well (Sonar family optimized for search). I think they were onto something, all model providers are very shallow when doing web search and hence hallucinate a lot even with search, Perplexity could've positioned itself as the most reliable LLM-based search. Unfortunately, their infra is abysmal for anyone who tries to use their API, and that's where a lot of the money could've come from (at least initially - it's very hard to compete with Google and OpenAI for consumer market)
What i'm trying to say is that principle is the same - startups don't work on fundamentals (revenue, retention), but rather on hacking the numbers to get the next round of funding. Pitching to investors becomes the skill in and of itself, same as writing grants. That said, since VCs are much more efficient capital allocators and, more importantly, much more hands-on in growing the distribution of startups than European accelerators are, their companies on average have higher success. But I think both are incentivizing the wrong skillset - the pitching skillset. This hurts a lot of companies that actually produce value (but struggle with "selling the dream" or "writing grants") on both sides