Charlotte Xia ✈️ CoRL

Engineer-turned investor l Co-founder @1943aiml | AI infra, robotics, and AI4S | prev @Formlabs, @Stanford, @Umich | Opinions my own

[Charlotte Xia ✈️ CoRL @xia_char]

Nvidia is reportedly in talks to acquire Reflection AI. On Vercel’s AI Gateway, open-weight share of tokens went from 7% in December to 56% in August, while taking just 14% of spend. By one day in mid-September, it hit 78%.

Anthropic shows the split. From June to July, its spend share rose from 61% to 64% while its token share slipped from 32% to about 30%. Pricing power is holding for now.

The usage data tells us an interesting pattern: open wins the token volume, closed keep the dollars.

The driver is agents looping in the background, planning, retrieving, and revising. Developers route that repetitive middle to open models at lower cost and save closed APIs for the calls where a mistake is costly.

So how would the token volume and dollars flow in the next few years?

[Charlotte Xia ✈️ CoRL @xia_char]

Robots gotta eat!!

[1943 @1943aiml]

We’re hosting a researchers’ lunch during #CoRL2026 in Austin with @Lianhuiq. Join us for Southern classics and conversation with researchers and builders pushing physical AI forward.

RSVP: https://t.co/MqWTC5uZrC

Thanks @Orbifold_AI @OtiliaBarbuta for sponsoring!

[Charlotte Xia ✈️ CoRL @xia_char]

Night one of CoRL this year, we’re taking physical AI after dark to a museum of oddities: shrunken heads, a frozen caveman, and retro computers booting up for the occasion.

Come join our happy hour on Monday 11/9: https://t.co/FOSriHJBhm

[Charlotte Xia ✈️ CoRL @xia_char]

@ollama Thank you for supporting open weight community!!

[Charlotte Xia ✈️ CoRL @xia_char]

Little Ornith be cooking! This time, the open weight is pretrained from scratch, achieved frontier performance, and can run on a single NVIDIA DGX. Congrats team!

[Ornith @ornith_]

Aloha!🌺 Meet Ornith-1.5-122B: No Tradeoff Between Strong and Fast

Stronger. Faster. Leaner. Ornith-1.5-122B delivers frontier-level capabilities with exceptional inference efficiency. With superior instruction following, stronger task decomposition, and deeper understanding of real-world workflows, it achieves state-of-the-art performance among open-source models of comparable size, while running on a single NVIDIA DGX card!!

Open weights under the MIT license.
Coding
✅SWE-bench Verified: 0.845
✅TB2.1: 0.813
✅CyberGym: 0.800
✅NL2Repo: 0.512
Agentic
✅HLE: 0.446
✅BrowseComp: 0.785
✅MCP-Atlas: 0.746
✅Toolathlon: 0.708

🤗Hugging Face: https://t.co/w1hD6rra1o
We can’t wait to see what you build with Ornith-1.5-122B.

[Charlotte Xia ✈️ CoRL @xia_char]

21x faster than Microsoft DeepSpeed and 7x than NVIDIA Megatron! Very exciting

[Physion Labs Official @Physion_Labs]

The AI industry has a massive compute efficiency problem. At Physion Labs, we think it's time to challenge it.

Today, we're sharing the technology behind NexTron, our world-intelligence training engine.

Our benchmarks on matched hardware:
⚡ Up to 20.9× SFT throughput vs. DeepSpeed (H100)
⚡ Up to 6.6× SFT throughput vs. Megatron (H200)
⚡ Up to 7.2× SFT throughput vs. Megatron (B200)
⚡ Up to 3.1× end-to-end RL throughput vs. Miles–Megatron
🔥 180B model fine-tuning on just 8 H100 GPUs.

Same hardware. Dramatically higher throughput. Comparable model quality.

We believe the next frontier isn't just building bigger GPU clusters.

It's unlocking more intelligence from every GPU, every watt, and every dollar spent on training.

#PhysionLabs #NexTron #WorldIntelligence #AIInfrastructure

[Charlotte Xia ✈️ CoRL @xia_char]

@zacklearner Thank you Zack!

[Charlotte Xia ✈️ CoRL @xia_char]

@mmmbchang Lmao

[Charlotte Xia ✈️ CoRL @xia_char]

@MrrrOzi This is the status on developers side for now but may change soon

[Charlotte Xia ✈️ CoRL @xia_char]

Nvidia is reportedly in talks to acquire Reflection AI. On Vercel’s AI Gateway, open-weight share of tokens went from 7% in December to 56% in August, while taking just 14% of spend. By one day in mid-September, it hit 78%.

Anthropic shows the split. From June to July, its spend share rose from 61% to 64% while its token share slipped from 32% to about 30%. Pricing power is holding for now.

The usage data tells us an interesting pattern: open wins the token volume, closed keep the dollars.

The driver is agents looping in the background, planning, retrieving, and revising. Developers route that repetitive middle to open models at lower cost and save closed APIs for the calls where a mistake is costly.

So how would the token volume and dollars flow in the next few years?

[Charlotte Xia ✈️ CoRL @xia_char]

Frontier intelligence shouldn’t stay concentrated in a few closed labs. The game on Western open weights has just begun. I wrote down some thoughts on open weight AI and the frontier escape velocity. If you have your own predictions, I’d love to compare notes.

https://t.co/hAODzO0UjS

[Charlotte Xia ✈️ CoRL @xia_char]

Prediction 1: The spend gap narrows. Closed labs lose per-token pricing power as open models match them on routine work, prices keep falling, and API bill shocks push enterprises to self-host and fine-tune open weights. But the spend pool itself restructures. Dollars relocate down to the metal and up the stack: to inference providers and neoclouds billing against GPUs, and to the application layer. Open weights become the most valuable strategic asset.

Prediction 2: This could slow if the frontier re-widens. Recursive self-improvement plus a compute edge could give closed labs escape velocity. They also own the product, see how people actually work, and train on that signal. Compute is the main bottleneck for open weight companies, and if it stops being binding, closing the gap is only a matter of time.

[Charlotte Xia ✈️ CoRL @xia_char]

@TracyyLiu @NousResearch @dillonrolnick @hbizira @Teknium @M12vc Congrats Tracy!

[Charlotte Xia ✈️ CoRL @xia_char]

@soydotrun @jasonwu0731 @salesforce @JiweiLi1 @ornith_ @kingsquare2 @nebiusai The event is in San Francisco, a great way to kick off CoLM :)

[Charlotte Xia ✈️ CoRL @xia_char]

Excited to be moderating a panel at our Continual Learning After Hours on Monday, featuring @jasonwu0731 (Jason Wu, Sr Director of @salesforce AI Research), @JiweiLi1 (Jiwei Li, Cofounder of DeepReinforce/@ornith_) and @kingsquare2 (Di Jin, VP of @nebiusai, Cofounder of Eigen AI).

Whether you're coming from @COLM_conf, making the rounds at #SFTechWeek, or just deep in the space, we'd love to have you join us! https://t.co/lS6M92S0zf

[Charlotte Xia ✈️ CoRL @xia_char]

@albert_ge_95 @Zhiqi_Gao_2001 Come join our Continual Learning Happy Hour on Monday! https://t.co/DHfqDsuEgW

[Charlotte Xia ✈️ CoRL @xia_char]

@_OliverStanley @ScaleAILabs Come join our Continual Learning Happy Hour on Monday! https://t.co/DHfqDsuEgW

[Charlotte Xia ✈️ CoRL @xia_char]

@hannawallach @MeeraDesai18 @ang3linawang Come join our Continual Learning Happy Hour on Monday! https://t.co/DHfqDsuEgW

[Charlotte Xia ✈️ CoRL @xia_char]

@MimansaJ Come join our Continual Learning Happy Hour on Monday! https://t.co/DHfqDsuEgW

[Charlotte Xia ✈️ CoRL @xia_char]

@beanie0__0 Come join our Continual Learning Happy Hour on Monday! https://t.co/DHfqDsuEgW