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/…

Centralized training assumes uniform hardware. Slow nodes bottleneck everyone. Raven's flexible updates let fast nodes continue while slow nodes catch up. - Fast nodes train without waiting - Slow nodes contribute at their pace - Heterogeneous h/w work together No bottlenecks.

Server market revenue up 97% YoY. AI server shipments growing 28% annually. GPU lead times 36-52 weeks. Memory prices could double by 2026. Hyperscalers pre-committing capacity years ahead. Raven coordinates existing distributed hardware, no lead times. acecloud.ai/blog/ai-comput…

AI training requires thousands of synchronized GPUs. One failure stops the entire job. Raven's fault tolerance handles failures. - Nodes hot-join ongoing sessions - Failed nodes replaced automatically - Training continues uninterrupted Zero downtime distributed training.

@kimmonismus Automated researchers running end-to-end need persistent, distributed compute not datacenter allocations that shift with internal priorities.

Holy: OpenAI says it’s reallocating compute and talent toward its next generation of models, echoing the internal shift that preceded GPT-3. The focus now is on “automated researchers” and agent-based systems that can execute complex tasks end-to-end. It’s a signal that the lab sees these systems as the next major step beyond current AI tools. Thats why they sacrificed Sora. For automated reserachers.

@VraserX Everything routing through intelligence needs compute that routes through everywhere. Distributed infrastructure is that layer.

People still talk about AI like it’s a feature. It’s becoming a layer on top of reality itself. Search. Work. Code. Media. Science. Everything is about to route through intelligence.

@CoinMarketCap FUTURE.

web3 is _____.

@ujjwalscript Real systems still need real infrastructure underneath them. Distributed compute doesn't vibe, it runs.

The “Vibe Coding” honeymoon is officially OVER. For a while, it felt magical. Prompt in, product out. No deep context, no architecture, no trade-offs. Just vibes. But reality is catching up: • Systems still need to scale
• Edge cases still exist
• Debugging still hurts
• And someone still has to own the code AI didn’t replace engineering, it amplified the gap between people who understand systems and people who don’t. “Vibe coding” is great for getting started.
But shipping real, reliable software? That still requires thinking. The engineers who win won’t be the ones who vibe the fastest - they’ll be the ones who understand what the vibe produced.

@kimmonismus Timelines moving forward means infrastructure decisions made today matter more than anyone planned for.

The authors of AI-2027 have moved forward their predictions for "AI timelines and takeoff speeds" in a brief post because, contrary to their expectations, the pace of development is accelerating. "Daniel’s Automated Coder (AC) median has moved from late 2029 to mid 2028, and Eli’s forecast has moved a similar amount. The AC milestone is the point at which an AGI company would rather lay off all of their human software engineers than stop using AIs for software engineering." (...) "In short, progress in agentic coding has been faster than we expected over the last 3-5 months. The METR coding time horizon trend has its flaws, but we still consider it the best individual piece of evidence for forecasting coding automation. On that metric, growth has continued at a rapid pace." But they are not alone in their analysis. It seems to be the general perception that the exponential development is occurring faster than expected. "Finally, some AI company researchers that we respect continue to say that automated AI R&D is coming soon; sooner, in fact, than we ourselves think." Highly recommend you sub to their substack and read their blog.

@slow_developer More thinking on GPUs means more demand for compute that isn't locked behind one provider. Distributed infrastructure handles that without the bottleneck.

Sam Altman says "AGI soon" is too vague for what is really coming A clearer way to say it: AI may soon do more of the world's total thinking than humans That could cause major short-term job disruption if most intellectual work shifts to GPUs "so we need to start designing an economy that works for everyone"

Deloitte: Post-training consumes 30x training compute, test-time scaling 100x. Inference hitting two-thirds of all compute in 2026. AI datacenter spending $400B this year, $1T by 2028. Demand accelerating, not slowing. Raven scales without building. businesstoday.in/technology/sto…

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