https://xitter.cf/t.co/IR1SKOTVBW Startups: https://xitter.cf/t.co/nuG9RProQy RL & data: https://xitter.cf/t.co/hceNvJcLge I read DMs. DM to suggest a topic for future surveys.
SF
Joined June 2013
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I co-wrote this RL environment FAQ with @datagenproc from @EpochAIResearch:
Frontier labs are investing massively in RL environments, yet most of what happens in this space stays behind closed doors.
@chrisbarber and @js_denain interviewed 18 people from RL environment startups, neolabs, and frontier labs. Here's what they found:
Chris Barber retweeted
I'm excited to launch Relational Capital, a $50m solo GP fund.
I spent the last 8 years as a founder, building @usekojo into the largest US construction procurement platform.
Now I'm backing founders at the earliest stages with $1m-$5m checks.
An abundant future lies ahead 🇺🇸
Chris Barber retweeted
We are releasing Ultramarine 1 Flash, a frontier-class model built for knowledge work. It is open-source and achieves similar performance to Fable 5, while being 60x cheaper.
It was post-trained by a team of 5 for ~$300k all-in. Available on API now, with weights releasing in 2 weeks.
I asked people: "who has hosted great events in SF?"
2 votes each:
- Alicia (@sharedalbums)
- Ben Lang (@benln)
- Dave Fontenot (@davefontenot)
- Ankit Gupta (@AnkitGuptaAI)
- Harbor (@alexgshaw, @ryan_marten)
- Joanne Shang (@JoanneShang)
- Midjourney (@DavidSHolz, @aalleexx)
- Ricardo de Arruda (@notawizard)
- Sarah Chieng (@MilksandMatcha)
- Sukone Hong (@sukonehong)
- Umesh Khanna (@forwarddeploy)
- Vivian Shen (@vivianmshen)
40 people voted. People couldn't pick themselves. I didn't vote.
Continued:
I asked people which tier they'd put each lab in:
Frontier:
- Anthropic: consensus
- OpenAI: consensus
n-1 (1 generation behind):
- Google DeepMind: consensus
- xAI: consensus
- Meta (Muse Spark): over half say n-1
- Moonshot (Kimi): over half say n-1, some n-2
- Zhipu (GLM): over half say n-1, some n-2
- DeepSeek: over half say n-1, some n-2
- Thinking Machines (Inkling): leans n-1, some put n-2
n-2 (2 generations behind):
- Alibaba (Qwen): leans n-2, some put n-1
- ByteDance (Seed): over half say n-2
- Xiaomi (MiMo): leans n-2, some put n-1
- MiniMax: over half say n-2, some n-1
- Tencent (Hy): over half say n-2
- NVIDIA (Nemotron): leans n-2, some put n-3+
n-3+ (3+ generations behind):
- Microsoft (MAI): over half say n-3+, some n-2
- Cohere: over half say n-3+, some n-2
- Mistral: over half say n-3+
- Amazon (Nova): consensus
- Apple: consensus
Consensus = 75%+, over half = 51%-74%, leans = the most common pick but under half, some = 20%+.
I asked 70 people. Engineers/researchers/founders at AI data cos, people at labs (I didn't count votes for their own lab), and founders/engineers at other startups. I didn't vote.
I asked people: "who have you met in the AI data and post-training space that you find most insightful?"
5 votes: Daniel Chang (@dnnssl2)
3 votes each:
- Abhijay Rana (@abhijaymrana)
- Ryan Wexler (@RyanMWexler)
- Sean Cai (@SeanZCai)
- Will Brown (@willcb)
2 votes each:
- Amy Tam (@amytam01)
- Jesse Hu (@huyouare)
- Nikhil Namburi (@nikhilvnamburi)
- Thomas Liao (@distributionat)
91 people were picked. The other 82 got 1 vote each and are in the next two posts.
People couldn't pick themselves. I didn't vote.
