Tokenising compute. The orchestration layer for AI, one execution layer across models, inference and compute.
Joined March 2026
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Some AI work is quick. A picture usually comes back in the same response. Video is not like that: a clip takes one to five minutes, and nobody should hold a connection open that long and hope.
So slow work on Corent works like a deli counter. You take a number.
A video request returns straight away with a job id. Then you choose how to wait. Poll the job. Or open its stream and get server-sent events as it changes, with no polling at all. Or pass a webhook URL and the full result is posted to you when it finishes, signed if you give a secret.
The part people like most: the charge lands on the job when the video completes, next to the delivered dimensions measured from the stored file. You never pay for a ticket that was not served, and you never have to look up what it cost later.
Images can do this too. Ask for async on a slow render and you get the same kind of ticket.
Take a number. Get on with your day.
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Tell me you use five AI providers without telling me.
Five keys in a password manager. Five dashboards. Five invoices that arrive on different days in different formats. Five SDKs with five ideas of what an error looks like. And a doc somewhere titled "which model do we use for what", last updated by someone who left.
None of that is your product. It is the tax you pay for wanting the best model for each job.
Corent puts one key in front of 1000+ models from 50+ labs. One integration, one bill. Let Corent pick the model, or name the exact one. Every response carries its own receipt: what served it, the exact charge, how long it took. On routed requests, failover happens inside Corent, and the request log tells you when it fired.
The pile on the desk is optional now.
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Your agents are in safe hands with Corent unified API
1000+ models.
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Stop betting your app on one AI model.
With Corent you're model agnostic: one API, 1000+ models. If one goes down, the next one answers. Your users never notice.
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corent retweeted
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.