AI can give you an answer. But can it prove why that answer should be trusted? Cournot’s Proof of Reasoning turns AI judgment into a verifiable process: 🔸Monitor real-world events. 🔸Aggregate evidence across sources. 🔸Reason through multiple agents. 🔸Validate the logic. 🔸Anchor the conclusion onchain. Cournot's AI Native Oracle doesn't provide a transparent, auditable path from evidence to truth.

AI coordination, ecosystem participation, and real onchain activity continued to evolve across the Pharos ecosystem this week 👇 🤖 AI x Crypto - @AnvitaFlow x Pharos Agent Carnival jointly brings the decentralized AI agent coordination network to the ecosystem and exploring how autonomous agents can discover, collaborate, and settle value onchain! - @AssetoFinance partnered with NablaZ to explore AI-driven capital management for tokenized RWAs, connecting institutional-grade yield assets with intelligent, non-custodial settlement infrastructure 🌐 DeFi & Liquidity - @FaroSwap shared new growth milestones, surpassing $250M in cumulative trading volume on Pharos, highlighting continued activity driven by real users, liquidity, and sustained market participation ⚡ Infrastructure - @zan_team continued expanding its Web3 infrastructure offerings, showcasing millisecond-level node performance designed to support builders and applications operating across the ecosystem 🎁 Community & Participation - @Top_nod Million Cup continued to grow, with the launch of a new Invite Leaderboard designed to reward community members for bringing new participants into the ecosystem 💙 Across the ecosystem, teams continued pushing the boundaries of AI-native finance, expanding onchain participation, and strengthening the infrastructure powering the next generation of RealFi applications ⚓

AI is going to make most company's product look/work roughly the same... unless you're bespoke to the customer. When the tools commoditize, feature parity and beautiful UI/UX stops being a moat. Anyone can build the same capability you did, a quarter later, with or without a large technical team. BUT, what they can't copy, is the trust you earn building it that way. And trust isn't built with AI.. it's built with real people. At @DaltonMillsAI we're building a team of the most AI-native builders in the trade. But we're also building the opposite of what you'd expect an AI company to build. Every prospect has my cell phone. Giving out your number is the fastest way to start building trust. But the number is just step one.. you earn it by actually answering the call/text, and the not quick, "quick question." Most people forget that part. The way we look at it, the more we automate the product, the more time it frees up to do things like this. We're using AI to get more human and to remove human headaches.

AI-native software engineering teams operate very differently than traditional teams. The obvious difference is that AI-native teams use coding agents to build products much faster, but this leads to many other changes in how we operate. For example, some great engineers now play broader roles than just writing code. They are partly product managers, designers, sometimes marketers. Further, small teams who work in the same office, where they can communicate face-to-face, can move incredibly quickly. Because we can now build fast, a greater fraction of time must be spent deciding what to build. To deal with this project-management bottleneck, some teams are pushing engineer:product manager (PM) some teams are pushing engineer:product manager (PM) ratios downward from, say, 8:1 to as low as 1:1. But we can do even better: If we have one PM who decides what to build and one engineer who builds it, the communication between them becomes a bottleneck. This is why the fastest-moving teams I see tend to have engineers who know how to do some product work (and, optionally, some PMs who know how to do some engineering work). When an engineer understands users and can make decisions on what to build and build it directly, they can execute incredibly quickly. I’ve seen engineers successfully expand their roles to including making product decisions, and PMs expand their roles to building software. The tech industry has more engineers than PMs, but both are promising paths. If you are an engineer, you’ll find it useful to learn some product management skills, and if you’re a PM, please learn to build! Looking beyond the product-management bottleneck, I also see bottlenecks in design, marketing, legal compliance, and much more. When we speed up coding 10x or 100x, everything else becomes slow in comparison. For example, some of my teams have built great features so quickly that the marketing organization was left scrambling to figure out how to communicate them to users — a marketing bottleneck. Or when a team can build software in a day that the legal department needs a week to review, that’s a legal compliance bottleneck. In this way, agentic coding isn’t just changing the workflow of software engineering, it’s also changing all the teams around it. When smaller, AI-enabled teams can get more done, generalists excel. Traditional companies need to pull together people from many specialties — engineering, product management, design, marketing, legal, etc. — to execute projects and create value. This has resulted in large teams of specialists who work together. But if a team of 2 persons is to get work done that require 5 different specialities, then some of those individuals must play roles outside a single speciality. In some small teams, individuals do have deep specializations. For example, one might be a great engineer and another a great PM. But they also understand the other key functions needed to move a project forward, and can jump into thinking through other kinds of problems as needed. Of course, proficiency with AI tools is a big help, since it helps us to think through problems that involve different roles. Even in a two-person team, to move fast, communication bottlenecks also must be minimized. This is why I value teams that work in the same location. Remote teams can perform well too, but the highest speed is achieved by having everyone in the room, able to communicate instantaneously to solve problems. This post focuses on AI-native teams with around 2-10 persons, but not everything can be done by a small team. I'll address the coordination of larger teams in the future. I realize these shifts to job roles are tough to navigate for many people. At the same time, I am encouraged that individuals and small teams who are willing to learn the relevant skills are now able to get far more done than was possible before. This is the golden age of learning and building! [Original text: ]

Chinese AI startups are bringing highly anticipated world models to China’s 680 million gamers. From instant 3D asset generation to endless interactive storylines, developers are racing to build a new era of AI-native video games

An AI agent can be approved to move money-and still execute the wrong outcome. Today, @nathan_sj_stem × @alexsrawitz are recording: The Control Stack for AI-Native Banking From Payment Permission to Verifiable Execution The challenge isn’t getting agents to act. It’s ensuring every execution remains within mandate. Coming soon on Vishwa Hubble.

How AI-native law firms use "management services organisation" structures to access capital historically barred from US law firms, including PE and VC funds (@stephenfoley / Financial Times) (Visit Techmeme dot com for the link and full context!)