Lots of situations, here are 2 I’ve faced recently (cannot give too much detail for privacy reasons, but should be clear enough)
1) low latency desired, long user prompt
2) function runs many parallel requests, but is not fired with common prefix very often. OpenAI was very inconsistent about properly caching the prefix for use across all requests, but with Anthropic it’s very easy to pre-fire
All these things are designed to create lock in for companies. They don’t really fundamentally add to the functionality of LLMs. Devs should focus on working directly with model generate apis and not using all the decoration.
I vastly prefer the manual caching. There are several aspects of automatic caching that are suboptimal, with only moderately less developer burden. I don’t use Anthropic much but I wish the others had manual cache options
Cerebras has very limited scale. Mistral has very few users so they can use cerebra’s in inference whereas OpenAI and Anthropic cannot. If mistral grows a lot they will stop using cerebras
The thing is, YC heavily signals against solo founders.
So it seems like there could be a lot of adverse selection in your data where good solo founders don’t bother applying, or good solo founders take on value destructive cofounders just to satisfy YC.
That’s then a vicious cycle where YC sees increasingly lower quality solo founders which reinforces their opinions and data about solo founder outcomes.
That’s my point. Even in massive outlier upside cases getting a 1% equity deal at seed is not really worth more than just being a standard FAANG worker. And that requires many rounds of funding an and dilution and an exit. It’s worth remembering exits are very hard right now and founders have far more liquidity options. There have been multiple unicorns I’m aware of in the past 5 years with founders taking 8 figures in secondary and employees getting nothing. Now those unicorns have worthless equity for employees and the founders are on the beach.
Would love to see the statistical evidence because if anything it seems to be the other way round and this is pretty much just reflective of YC’s personal preference as it were (and many VCs prefer otherwise). Personally I would only advise someone to take on a cofounder if they already know them well and need their skill set. There is so much risk in a poor fit that it far outweighs any theoretical benefit.
Yes and I recommend that people interested in GPT-4 use this service as it’s isolated from OpenAI and it is the absolute best model right now.
That said, there are 3 quite worrying future possibilities, both stemming from the level of investment and commitment by Microsoft in OpenAI - a huge percentage of Azure’s value is bet on an exclusive partnership with them. That gives OpenAI a lot of leverage. And customer data is a very tempting cookie jar to build a long term moat for these companies
Possibility 1: someone overtakes OpenAI models and you’re stuck on Azure who won’t offer that model
Possibility 2: OpenAI decides to break with Microsoft and you’re stuck with a useless application. AWS Bedrock is far less likely to leave an AI application obsolete.
Possibility 3: OpenAI put pressure on Microsoft to loosen the data protections around the service, or go around them entirely. This is a particular concern as the systems in the service become more complex and agentic and more difficult for Microsoft to audit. Model weights are highly opaque and Microsoft cannot trace the exact possible behaviour of these systems. What if GPT-6 changes its own weights during inference for example? How can Microsoft ever understand if that’s a true critical piece of functionality or a proxy method to access customer data etc.
1) OpenAI has consistently gone back on commitments it has made
2) Sam Altman has a shady track record publicly, and if you believe the things people say privately he has consistently done business very dishonestly throughout his career. He is the CEO, and virtually the entire executive team are people he brought in from his network. It’s his company.
3) To give one example of many, OpenAI recently changed the terms of ChatGPT so that web users conversations can now be trained on (and if you want to save any chats you must opt in). Presumably this also applies to all conversations you had under the old policy despite saying they would never train on those conversations.