IOSF✓

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.

1/ One AI bill, 14 models. DeepSeek V4.1 Flash handled 53% of the tokens for 2.7% of the spend. $0.02 per million tokens. The bill's average: $0.41. The trick isn't the model. It's the cache. 🧵

6/ The catch: fresh prompts pay $0.15-0.30 in, $0.60-1.20 out weekday peak hours cost 2x cache hits need an exact prefix match prices rose 50% to 1,100% by rate in Aug Anthropic has twice accused DeepSeek of distilling Claude

7/ For agents, price per token is the wrong number. Watch the price of a new token, and how much of each request a provider can serve from storage. Sources:

China's humanoid boom: the industry will win. Most companies won't. 150+ companies. ~50 mass-production models. Sub-5% margins on entry units. Don't bet on who wins — back the layers that win either way. 🧵

Robots are still in school. Valuations already graduated. 74% of a leading Chinese humanoid maker's shipments went to universities. Funding: RMB 93.5B in H1 2026, ~5x YoY. Unitree: ~61% off its post-IPO high. The industry is in Phase 3. Capital is paying for Phase 6. 📷

China's embodied AI industry is still in its capitalization phase, but capital is pricing it as if robots already earn their keep. Domestic embodied AI funding hit RMB 93.5B in H1 2026, about 5x year on year. Valuations have already peaked. Unitree opened 629% up on its STAR Market debut, then fell about 61% from its high within two months. September humanoid funding dropped about 73% from August. Three points from @0xjacobzhao's new piece in our Physical AI 2026 series: → Don't wait for AI-style earnings pass-through. About five hyperscalers buy compute, and they buy defensively. Thousands of plant managers buy robots, and they always have an alternative: people. → Overheating hurts OEMs directly. Compute, tactile sensing, simulation assets and scenario operations aren't priced off robot ASP. → The first real signal will come from a single vertical where unit economics work and replicate: payback under 18–24 months, plus repeat purchases. For founders, the pricing anchor is moving from scarcity to revenue quality. Lead with buyer-calculated ROI, manual-intervention rates and cross-year repeat orders, not shipment counts.

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?

GPU rent is the crude. AI tokens are the gasoline. But who hedges the margin in between? For the same AI model, prices can vary up to 4× across providers. GPU futures alone won't solve that risk. So who writes the grade? Hedging Intelligence | Part 2 📷

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 👇

RT @IOSFVC: 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.

@typesafeai Full Report：