YAI @YAI_Protocol

Decentralized AI compute network. Up to 80% more cost-efficient inference APIs for builders, smart agent chat for teams, yield for GPU owners. yaiprotocol.com Global Joined February 2026

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YAI @YAI_Protocol

a day ago

@Cryptosohail @hare_xyz @tryimoo Probably?? 😉

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The AI compute flywheel we are building at YAI breaks down into three connected sides: 1. Hardware owners plug in idle GPUs to run real, verified AI tasks and earn steady yield. 2. Builders and teams consume that capacity through a low-cost, secure API with zero data logging and complete privacy. 3. Users and businesses manage multi-agent workflows in our smart workspace with strict budget caps. Supply earns yield, builders cut compute bills with private execution, and the chat acts as the live proving ground.

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YAI @YAI_Protocol

6 days ago

If you are at @token2049 Singapore and tired of paying absurd cloud bills for your model calls, keep an eye out for us. Our team will be roaming the floor looking for companies to become our initial design partners. You get full access to our compute layer, legacy status, and our undivided engineering focus. Drop a DM if you want to grab coffee, compare benchmarks, or just talk shop. Spot the tee with the [Y].

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YAI @YAI_Protocol

2 weeks ago

A lot of teams discover mid-sprint that their inference provider logs prompts by default. Then they go hunting through docs for an opt-out that may or may not exist. YAI retains nothing after the response is delivered, that's the architecture, not a settings toggle.

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YAI @YAI_Protocol

3 months ago

Model competition prevents one form of concentration. It does not solve the next one. Even with open models, inference can still depend on a small number of centralized providers. An open model layer needs an open execution layer beneath it.

Gavin Baker @GavinSBaker

3 months ago

Kimi K3 may be an important inflection point for AI. Potentially negative for Anthropic and OpenAI while being net positive for essentially every other company in the world. I mean that very literally. Although the real “Sputnik moment” would be an open-source frontier model that

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YAI @YAI_Protocol

3 months ago

Open models are only half the shift. As model margins compress and token demand expands, value moves toward the infrastructure that can execute each token at the lowest reliable cost. The next cost curve starts with compute that is already there.

Gavin Baker @GavinSBaker

3 months ago

The mega bull case for AI infrastructure would be *if* market share shifted away from certain frontier labs with 90%+ inference margins toward cheaper models, whether open-source or closed. It would increase the ROI on AI spend for end customers by increasing intelligence per

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YAI @YAI_Protocol

3 months ago

Karp is right. It’s not only about which model is best. It’s about who controls the compute, who owns the data and who can verify what actually ran. These cannot be solved by trust alone. These are the same issues that shaped YAI from inception. This is why we’ve placed privacy and proof into the execution layer.

Palantir @PalantirTech

3 months ago

Palantir CEO Alex Karp on what customers actually want, the real business of frontier labs, and the importance of open source models: “What the technical customers want is control over their compute, their models, their data stack, and their alpha. They want to know they own the

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YAI @YAI_Protocol

3 months ago

A lot of AI infra stops at one side of the market. Either demand aggregation, or compute supply. YAI is focused on the full loop. Connecting demand to useful compute, verifying the work, and making sure compute operators get paid.

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YAI @YAI_Protocol

4 months ago

Inference demand is scaling faster than centralized infrastructure can efficiently absorb. Underused GPU capacity already exists. YAI turns that mismatch into verifiable useful work.

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YAI @YAI_Protocol

4 months ago

Goldman is describing the next AI margin question. Agentic AI multiplies token volume while unit costs fall. The question is whether that efficiency becomes hyperscaler margin or lower-cost execution for the market. YAI is designed to push that efficiency back into the market.

Goldman Sachs @GoldmanSachs

5 months ago

As consumers and enterprises adopt agentic AI technology, token consumption could see a 24-fold increase by 2030, according to Goldman Sachs Research. Read more: click.gs.com/g1ht

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YAI @YAI_Protocol

4 months ago

Brian is pointing at the real shift. When demand for intelligence keeps expanding, the bottleneck becomes execution. Where it runs. How it is verified. How privacy is preserved. YAI chose decentralization because this is where the future of AI is heading.

Brian Armstrong @brian_armstrong

4 months ago

Good take My guess is - demand for intelligence is near infinite - but 80% of workloads will be running on 99% cheaper models within 12-18 months - 20% of workloads will still run on latest gen models where IQ maxing is important (scientific breakthroughs, higher level

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YAI @YAI_Protocol

4 months ago

The YAI whitepaper is live. It lays out our approach to efficiently coordinating inference demand with decentralized AI execution. Available now y-ai.io/whitepaper/ind…

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YAI @YAI_Protocol

4 months ago

AI inference is becoming the cost center of the AI economy. Not just bigger models. Not just more GPUs. The next layer will coordinate decentralised GPU supply into better economics, private execution paths, and proof of useful work. We are building YAI to make it real.