State of Onchain AI Narratives > Privacy 2.0 leads in performance in the last 30 days, across FHE, MPC, TEE players @zama, @Arcium, @PhalaNetwork, @nillion > GPU financing has also been performing well with @USDai_Official getting the largest loan (~$129M) yesterday > Robotics looming with catalysts — @peaq x @codecopenflow Machine(.)fun + several Robotics @bittensor subnets incoming > Inference remains the #1 onchain AI narrative across 2026 with @AskVenice consistently growing its platform + DIEM ecosystem, @AskSurplus hitting ATH (~130B+) in daily tokens served, and other players continuing with their product developments (e.g. @engyai, @dphnAI, @openservai) > Bittensor is recovering nicely following the wave of stonk/token pairing meta, dragging the entire ecosystem of subnets upwards in PA > While the Agentic Economy/x402 hasn't found PMF or hit wider adoption this year, it has managed to organic adoption among power users, devs, and startups looking for lower cost, better micro-accounting tools & inferences. Several x402 facilitators (e.g. @PayAINetwork, @daydreamsagents) experienced positive PA as a result > Interestingly, Data is performing well thanks to @grass finally listening to the community, improving its comms, with better revenue transparency, and plans to accrue value back to token holders All in all, possibly one of the most bullish time to be investing + building in onchain AI More analysis on this in the upcoming After Hour EP.74 on Substack this Sunday

do you know the best part about exploring @openservai ? the reasoning model is actually top tier, it allows for major experimentation of how agents can interact with each other i made one such experimentation called servpit. there are 6 individual agents here all powered by serv reasoning and all thinking for themselves to handle decisions that suit their toggled model. once i’m done cleaning the bits, it will be available for you to see and use.

Why I’m excited to partner with @openserv: they build infrastructure for agents that actually DO things. I build worlds where those agents can play with you. Waypoint, FableBoy, Shiny Things. Just the beginning. Launch: launch.openserv.ai/projects/… TG: t.me/+a8nadiFIEFFjOWNh

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Jev + SERV is actually insane. We already showed you can increase Jev's performance with SERV Reasoning. Now we're taking it further, bringing Jev-powered Decision nodes into Graph Sharding with the upcoming SERV v3. Here's a breakdown of how it works: Jev is a decision-making model. Given a task and a set of options, it predicts which path is more likely. Think of the octopus that predicted World Cup results. Jev does that for your business, except it's not luck. It weighs every option and tells you how sure it is. It does this by assigning probabilities to outcomes. It doesn't generate text on its own, so you can't expect it to create a new outcome for you. But that's also what enables it to be lightning fast and dirt cheap. For example, in customer service you can ask Jev how to triage an incoming query and route it to the correct department. It can only select from the list of departments you provide it. This also means it can't hallucinate a new outcome outside the options it's given, which makes it incredibly interesting for OpenServ. In Graph Sharding, we take a single system prompt and break it down into multiple LLM steps with deterministic input and output shapes. Some of these steps require an LLM to produce new output, while others are simply decision routers that determine the next possible path. Traditionally, LLMs are slow and expensive. Breaking a single prompt into multiple steps increases accuracy and reliability by a ton, but it also introduces latency. Jev takes on those decision nodes, which are the backbone of a business process and therefore SERV graphs, and makes them super consistent and lightning fast, lowering the overall cost and latency of graph execution. SERV Reasoning on its own is a great force multiplier for Jev because, like all other models, it works by interpreting input instructions. The clearer those instructions are, the better the model performs. That's where SERV Reasoning comes into play. Just like amplifying any other model, we also amplify the accuracy and consistency of Jev's responses. And now we're bringing Jev-powered Decision nodes into Graph Sharding with SERV v3.

Jev is seriously impressive. SERV makes it better. We put Jev through the same benchmark we use to evaluate leading AI models, testing it both standalone and armed with SERV Reasoning. Paired with SERV, Jev moves into the top tier of our benchmark, beating Claude Fable 5 at 30x lower cost, with cost similar advantages over GPT-5.5 (~15–20x), Gemini 3.5 Flash (~15x), and Grok 4.3 (~6–7x). The result is clear: Jev + SERV materially outperformed Jev on its own. Jev is extremely fast, well designed, and great at decision-making. Instead of relying on open-ended responses, it produces structured decisions with probability scores attached. SERV pushes that performance further, adding a stronger reasoning layer to improve the quality of those decisions for high volume agentic work. The SERV Reasoning API is live, so any developer can test and deploy SERV Reasoning today in just a couple minutes. Get access at: openserv.ai The agentic economy won’t scale on better models alone. It needs better reasoning infrastructure that are finetuned to the reliability, auditability, and affordability requirements of enterprise agents. SERV is building that layer.

The agentic economy will be powered by SERV. SERV Reasoning is live: enterprise-grade AI infrastructure for developers. Entirely new markets, products, and apps will emerge around autonomous agents. Build before v3 → v4 takes SERV further. Apply: openserv.ai/hackathon

Build with SERV Reasoning across four tracks: - Robinhood Mainnet & MCP - Coinbase AgentKit - @IXSFinance RWA Vaults - Open Track Push SERV across RWAs, tokenized stocks, automated trading, and entirely new agentic use cases. Learn more about SERV ↓ docs.openserv.ai/what-is-ser…

AI agents will manage trillions in capital across financial markets. Join the Spaces on Sep 22 with OpenServ Founder & CEO @open_founder and @IxsFinance CEO @julian2kwan, to explore what developers can build with SERV - across RWAs, tokenized stocks, automated trading, and more. As AI takes on increasingly complex financial decisions, managing real capital at scale requires better decision-making. With SERV Reasoning, developers will finally be able to build smarter, more reliable agents. Build the future of agentic finance today, and join the 1st SERV Hackathon: openserv.ai/hackathon Set your reminder below.

After co-inventing ChatGPT, I kept asking myself: why have superhuman chat models not led to AGI? I’ve spent the last 2 years in stealth building a new way to train models (RLCD), and a new type of frontier AI model that we are releasing today: Jev • 20-200x faster • 40-400x cheaper (w/ output tokens free) • Frontier composable intelligence optimized for decisions AFAICT the shortest path to AI-based economic revolution