Raven Protocol 🐦‍⬛

🏆Raven wins the @mozillabuilders Award at the Fix-the-Internet showcase. Recognition by the top open source organization in the world that Raven Protocol is a core infrastructure project. We demonstrated best traction and commitment to fix the internet! medium.com/ravenprotocol/…

Q1 2026: AI everywhere. Q2: even more AI. Q3: way more AI. Q4: you get it. Usage isn't peaking, it's just starting. Centralized clouds already sweating. Raven Protocol doesn't sweat. Distributed nodes scale with the chaos, not against it.

AI adoption isnt slowing down, its accelerating. More agents, more workflows, more users, more inference. Demand only moves in one direction: up. Centralized infrastructure hits limits. Raven doesnt, it scales horizontally across thousands of distributed nodes as usage grows.

GM! Breaking: We've solved the AI crisis by asking datacenters to work harder. JK. You can't scale trillion-dollar demand with "wait 7 years for grid connection." Raven Protocol coordinates existing hardware globally: no construction timelines, no queues, no April Fools.

Autonomous agents need infrastructure that scales with them not against them. Thousands of agents running 24/7 workflows cant wait in cloud queues or compete for datacenter slots. Raven Protocol deploys capacity instantly across global nodes without centralized approval delays.

Hyperscalers spending $690B on AI infrastructure in 2026, nearly double 2025. Microsoft $80B backlog unfulfilled, Amazon capacity-limited. Inference 55%+ of spend. Always-on agents, persistent compute, demand outpacing buildouts. futurumgroup.com/insights/ai-ca…

AI companies spending 40-60% over infrastructure budgets. Monthly bills: $2-5M for Fortune 500s. Inference now 15-20x training costs, GPT-4's $150M training became $2.3B inference in 2 years. Hardware refresh needed annually or overpay 30-60%. lloydson.com/insights/ai-in…

Agentic AI market hitting $8.5B in 2026, $45B by 2030. 75% of companies investing in agents driving compute 100x. Claude Opus sustaining 14.5-hour tasks, doubling every 123 days. Week-long agent tasks arriving late 2026. Infrastructure can't keep pace. Raven is the solution.

11 GW of 2026 datacenter capacity stuck in announced stage with no construction signs. Grid connection wait times hitting 7 years in US. 10+ moratorium proposals last month alone: NY, Michigan, Virginia, Oklahoma. Political limits biting hard. axios.com/2026/02/24/ai-…

Microsoft reports $80B Azure backlog unfulfilled due to power constraints. Amazon could grow faster but capacity-limited. Grid connection delays extending timelines 24-72 months. Raven's nodes already connected to power: no grid queue, no construction wait, deploy immediately.

@elonmusk The compute infrastructure behind that volume can't be centralized. Distributed is the only architecture that scales.

AI content will vastly exceed all human content

@a16z Adoption at that scale needs compute that reaches everywhere, not just where datacenters exist.

"The winner of the AI race will not be the company with the smartest model. It will be the company that is most effective at making the local hero—the teacher, the accountant, the community leader—ten times more powerful. Because in the end, intelligence travels through systems, but adoption travels through people." @heysakina on what she learned growing YouTube internationally and what it means for the AI race: a16z.news/p/the-sovereig…

@KobeissiLetter Agents replacing software subscriptions still need compute to run 24/7. Distributed infrastructure is that foundation.

Software companies fear AI agents could disrupt their entire industry: A record 27 US public software firms identified AI agents as a competitive risk in their filings in Q1 2026. The number has more than doubled since Q2 2025. By comparison, in Q4 2024, just 2 firms flagged this risk in their filings. Anthropic and OpenAI are releasing AI superagents which can use enterprise software the way humans do, but in a fraction of the time and without supervision. AI agents could replicate existing software or reduce subscription revenue, as companies that need fewer employees also need fewer software licenses. Investors are already pricing in the risk, with the Software ETF, $IGV, down -21% year-to-date. AI is reshaping the software industry.

@Yuchenj_UW Distributed compute doesn't need a kernel engineer on call. It coordinates infrastructure that already exists.

The highest-paying jobs today may be first in line for AI. Take GPU kernel engineers: a golden ticket to OpenAI or Anthropic, million-dollar offers. Now people're rushing to learn it. It takes ~1 year to get decent. But will Claude Opus 4.9 or GPT-6 beat you, or even the best kernel writers in the world? It’s already happening.

@mishadavinci Distributed compute is how decentralized intelligence actually runs - not just a principle, live infrastructure.

BigAI is not making intelligence more available and accessible. They are attempting to steal all human knowledge. And make you pay them for it. Decentralized intelligence will win.

@AlexFinn Hard niche problems need compute that scales without enterprise contracts. Distributed infrastructure lets the builder focus on the problem.

AI didn't kill software. AI just killed dumb software. Everyone and their mothers is building social media cross posting tools right now. Every day someone else releases a way to post to X/youtube/tiktok at the same time That's dead. What isn't dead is solving challenging, niche problems This is the secret to getting rich in the age of AI Instead of vibe coding the easiest thing you can think of like an AI content generator, go after a really difficult niche problem • An AI agent for grocery stores • A CRM for spas • An inventory management tool for retro video game stores Instead of opening Claude Code and vibe coding another wrapper, go after the hardest niche challenge you can think of THIS is how you win right now.

@JuliaEMcCoy Compute handles the machine work. Distributed infrastructure keeps the exit door open for everyone.

We were never meant to be human *doings.* We are human *beings.* But the industrial economy needed bodies at desks. Needed fingers on keyboards. Needed souls chained to output quotas. AI is dismantling that machine. The tragedy isn’t that AI is replacing workers. The tragedy is that so many people still *want* to be replaced by that machine. Wake up. This is your exit door.

@PeterDiamandis 8 billion users need compute that doesn't route through three datacenters. Distributed infrastructure was built for that scale.

I mean it truly when I say we are early to AI. ChatGPT has an average of 67.7 million users while Claude has approx. 11 million. There's 8 billion people in the world AND counting.

@balajis Infrastructure has the same rule; use what's efficient, distribute what scales, don't over-engineer what already works.

AI is a shortcut. So it’s useful. But it’s lazy. So it speeds execution. But it hides complexity. So you want to use it. But definitely not overuse it. So the user of AI often loves it. But the reader of AI often hates it.

@slow_developer The architecture decisions still need compute that scales with the implementation. Distributed handles both without the bottleneck.

writing code manually is boring now codex and claude code replaced the keyboard for me, but i still make the main architecture decisions -- then i use another agent to review the implementation and catch bugs or edge cases i missed AI with human guidance is a superpower

OpenAI COO: "Right now, it's memory" as AI bottleneck. TSMC capacity choking 2026 supply. Only 33% of 241 GW disclosed capacity under active development. Real bottleneck: "time to compute", physical world can't keep pace. Raven doesn't build. bloomberg.com/news/articles/…