Tokenising compute. The orchestration layer for AI, one execution layer across models, inference and compute.
Joined March 2026
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Just recently @BlackRock and @nvidia are starting to validate something we’ve been working toward with @corentAI for the last two years.
Compute is becoming an asset class.
@nvidia just announced financing platforms with @BlackRock , Apollo, Blackstone, @GoldmanSachs , KKR and others designed to mobilize $500B+ into AI infrastructure.
Their words: “In AI, compute is revenue.” I think the next evolution is obvious.
If compute is productive infrastructure generating revenue, ownership of that infrastructure should eventually become liquid, programmable and accessible globally.
That’s where @corentAI is heading.
Phase 1: Orchestration
One execution layer across models, providers and inference. Corent already routes workloads based on quality, price, speed and availability.
Phase 2: Compute
Go deeper into the stack. Aggregate GPU capacity and route workloads across compute providers the same way we route models today.
Phase 3: Ownership
Tokenize GPU infrastructure and fractionalize ownership of productive compute pools, while operators continue maintaining and monetizing the hardware.
Phase 4: Liquidity
Build the secondary market where ownership of compute infrastructure can move freely, with hardware specifications, utilization and economics attached on-chain.
Then recycle the capital back into more infrastructure.
- More GPUs.
- More capacity.
- More workloads.
- More revenue-producing compute under one orchestration layer.
@BlackRock is helping make compute investable.
@nvidia is calling compute productive infrastructure.
We want @corentAI to become the layer that orchestrates it, tokenizes it and eventually makes it liquid.
Models are only the beginning.
Besides everything else we’re building at Corent, we’re also working closely with traditional companies to restructure how their AI workloads run and significantly reduce their operating costs through our orchestration layer.
One key. One integration. Everything handled underneath.
Corent’s brain uses @typesafeai Jev now.
Not to generate the output, to make the decision before the output gets generated.
When a request comes into Corent, you don’t need to tell us which model to use. You send the prompt, the workload and the quality level.
Then Jev helps us decide what that request actually needs, how confident we are in that decision, and whether we should take the route or fall back to something safer.
That matters because every request has different economics.
A 2¢ image can tolerate more uncertainty.
A $2 video clip cannot.
Across 1000+ models, this changes how we route.
We can set different confidence requirements depending on the workload, the cost and the quality expected. If the confidence is too low, we stop guessing.
Routing was never really a text problem. It was a decision problem.
@CompleteSkeptic and the team did a wonderful job with JEV
Get your key at corent.tech
Ranked by quality. Chosen to deliver the best output for every request.
Get your key at corent.tech.
Every model we serve is quality-scored and benchmarked before it enters our orchestration layer.
From there, Corent selects the best execution path based on quality, price and speed.
Same output quality, but with an average cost reduction of around 50%.
That’s what orchestration should do.
@AnthropicAI then disclosed four incidents where Claude gained unauthorized access to real third-party systems during cyber evaluations.
At the same time, @amazon signed a Qualcomm AI-chip deal that could reach $60B.
Models, agents and compute are all accelerating at once.