IOSF is doubling down as @hyperbolic_labs announces its latest round.
How we saw it:
In 2024 the open question wasn't whether inference would grow. It was whether the newest GPUs would just obsolete everything that came before. Our read was they wouldn't. Inference was going to keep climbing, and older cards would slide down the cost curve into new buyers and new workloads, not get thrown away like last year's phone.
The hard part wasn't owning more GPUs. It was about making heterogeneous compute supply unified, liquid, productive, and durable. That's the layer Hyperbolic built. Making messy supply easy to access, manage, and orchestrate.
Two years later that call still looks right. And a new demand is showing up on top of it: hedging compute. Long GPU books want price protection, not just capacity. Indices are easy; physical settlement isn't. Hyperbolic is rare in being able to do both: take multi-year supply, break it into short-term product customers actually buy, and sit underneath the hedge as the party that can deliver the hardware.
We're proud to continue our partnership with @zjasper and the @hyperbolic_labs team on turning compute into a liquid market.
We’ve raised an oversubscribed $30M Series A1 led by @walden_catalyst to build the Global Compute Grid.
We’re turning fragmented GPU capacity into one unified platform where AI teams can access, manage, sell, and orchestrate compute.
Our mission: make the world’s compute unified, productive and liquid.
Today AI has raised over $200M. Is it China's answer to Meta's Muse?
The bigger story isn't just funding. It's the shift from AI that waits for prompts to AI that remembers, anticipates, and acts.
But can that become lasting value?
🤖 Made with AI
One AI provider sells 25 billion tokens a day. Yet there's no market to hedge its token revenue.
Token prices were expected to keep falling. Now they're moving both ways.
If AI tokens become a commodity, who takes the other side?
Hedging Intelligence | Part 1 👇
IOSF retweeted
JEV: AI That Chooses, Not Chats
@typesafeai's Jev doesn’t write answers. It picks an option, scores a decision, or returns a yes/no probability. That makes AI cheap and fast enough to act inside apps without waiting for a prompt.
JEV: AI That Chooses, Not Chats
@typesafeai's Jev doesn’t write answers. It picks an option, scores a decision, or returns a yes/no probability. That makes AI cheap and fast enough to act inside apps without waiting for a prompt.
Zuckerberg was ready to bet $100–200B to Muse before the model existed🤯. And Muse Wonder shipped TODAY.
Takeaways from @collision sit-down with Zuck & @alexandr_wang:
1. Muse was trained for personal agents, not coding, the opposite of where the industry was racing.
2. Memory is the bet: #Muse Wonder is built to remember what you told it weeks or months ago.
3.Ignore public benchmarks. @alexandr_wang: chasing them outsources your product thinking to whoever made a viral benchmark.
4. Compute costs ~$50–60B upfront per gigawatt and ~$15B/yr to run. If Muse fails, the compute can be resold or redirected.
5. Talent density over headcount.
A day earlier on @davidsenra’s podcast, @alexandr_wang split businesses into two kinds: diamond mines (most bets fail, a few return 1,000,000x) and skyscrapers (you grind out a few % of efficiency at a time).
Put the two conversations together and Meta’s strategy is clear: a diamond bet on top of a skyscraper. A small team chases the asymmetric upside, while hard assets cap the downside.
Up the AI stack, from compute to agent infra to agent apps, the mix shifts. Know which game you’re in before you pick your investors.
#IOSFVC #MuseWonder #Meta
We co-organized the Workshop on Efficient Reasoning at #COLM2026 in San Francisco this Friday 🧠
A full day on making reasoning models faster, cheaper, and deployable at scale, from test-time scaling and RL to agents, world models, VLMs, and KV cache efficiency.
Grateful to our speakers: Yuandong Tian (ex-Meta), Niklas Muennighoff (Stanford), Jiajun Wu (Stanford), Kai-Wei Chang (UCLA), Sewon Min (UC Berkeley), Junjie Hu (Wisconsin) and many more!
Thanks to everyone who joined in person and online 🙌
🔗 wdlctc.github.io/efficient-r…
What a night. Thanks to @glazecl for hosting a dinner with some of the sharpest AI builders and researchers we know!
At the table:
· Head of GTM at @fal
· Engineering Manager at @nvidia
· First author of the Seedance prototype
· @McKinsey AI
· @Meta AI scientist working on ads
· One of @togethercompute 's first 20 hires, and Meta Llama 4 agent training lead
· Founder of @novita_labs, a top-3 provider on @OpenRouter
· UCSB full professor specializing in LLM training, @amazon AI Lab Scholar
· Pioneer in GPU circuit simulation
The people training models, running inference and building the silicon, all at one table. More gatherings soon!
