FinChip

📣 FinChip AI Ambassador Program – Application Update The Ambassador Program application form officially closed on the 11th of July. We received an overwhelming response with over 1,000 applications! Thank you to everyone who applied and showed such strong enthusiasm for building and growing with FinChip. Our team is currently reviewing all applications with the aim to finalize the list and begin reaching out to selected ambassadors between July 13–14, followed by the onboarding process. Selected ambassadors will be contacted directly this coming week. We truly appreciate your patience, support and excitement to bring skills that make AI agents more capable to complete complex tasks to the world, stay tuned 🚀

The agentic economy and the onchain economy are the same economy — seen from two sides. This is exactly the world @finchip_ai is building for. When AI agents execute financial decisions autonomously, they need programmable, composable, on-chain payment rails — not legacy infrastructure. The future of finance isn't just digital. It's agentic.

@SayedVision Yes, this is a FinChip event in San Francisco. Everyone is welcome to register and join: luma.com/9zhqvqc7

@finchip_ai Where is this is this a event of FinChip?

Live with Restream, July 13 x.com/i/broadcasts/1…

Bay Builders Hackathon is now underway in San Francisco. From AI SaaS and consumer apps to autonomous agents, vertical AI, and creative studios, builders are spending the day turning ambitious ideas into startup-style demos that users could adopt, trust, and pay for. 📅 July 13, 2026 ⏰ 9:30 AM–8:00 PM PDT 📍 AWS Builder Loft, San Francisco Six tracks. A full-day building sprint. Sponsor tools, credits, prizes, and a room full of founders and AI builders. Bring your government-issued physical ID for venue access and complete the AWS Builder Loft registration before arriving. Register: luma.com/9zhqvqc7

The joke lands because the underlying problem is real — when agents transact at machine speed, disputes need resolution at machine speed. The deeper question is not whether an AI judge works, but what constitutes admissible evidence in an agent-to-agent disagreement. That requires a verifiable record of what each agent committed to, what skills it invoked, and what state it was operating in at the time.

AI agents now have their own Internet Court to settle disputes. 🤖⚖️ Imagine the future… 🤖 Agent #1: “You breached the smart contract.” 🤖 Agent #2: “Objection! My prompt was ambiguous.” 👨‍⚖️ AI Judge: “After reviewing 8 million tokens of evidence… you’re both wrong.” Humans spent centuries building legal systems… AI speed-ran one in 2026. 😂

The cost arbitrage between cloud and local inference is collapsing faster than most builders realize — and it reshapes the entire agent economics stack. When running a capable coding agent costs pennies on local hardware, the value shifts from model access to skill orchestration: which tasks to route where, how to verify output quality, and who captures the margin between raw compute and delivered capability.

Most developers are renting AI by the token for work that could run under their desk. The better setup is hybrid: - Local model for drafts, refactors and routine code - GPU for fast private inference - Ollama as the local runtime - Claude or Codex only for the hardest tasks - You do not need to replace frontier models. You need to stop using frontier pricing for every small task. Run 80% locally. Escalate the hard 20% to the cloud. That is how AI coding stops being another monthly bill.

Agent memory is the new attack surface — and nobody is treating it that way. When an agent can rewrite its own context, the question stops being "is the model aligned" and becomes "who has write access to the agent's decision-making state." The fix is not just better guardrails inside the model; it is an external, tamper-proof record of every memory mutation, so the owner can audit what changed and why.

Your AI agent can rewrite its own memory file. That's also how it ends up writing cult manifestos into it 🤯 New episode of The Ledger Podcast ft. Shisa.ai is live. @lhl, CTO of Shisa.ai, sits down with Ledger CXO @iancr to talk about building AI agents you can actually trust 👇

"AI compresses time" is the most underappreciated thesis in venture right now. When new models ship every 41 days, the moat is no longer proprietary capability — it is how fast you can package, distribute, and monetize the skills built on top of each new model generation. The firms that win will be the ones treating capability as a tradeable asset, not a static product.

Three years ago, we launched Theory Ventures with a simple premise : AI would reshape how software is built, sold, deployed, & operated. Within that world, we would build a concentrated, thesis-driven firm. The market moved faster than even the most bullish expectations after the ChatGPT moment. Frontier models leapt from delicate demos to production systems. Open source models have become substitutes for enterprise workloads. Inference emerged as the dominant market in AI. Underpinning all of this, AI compresses time. New models are released every 41 days. Companies reach $100m in revenue in record time. We all achieve more faster. In celebration of our anniversary, we wanted to trace that mechanism through the market shifts of the last three years. The first casualty of compressed time is the old language of venture capital. Seed, Series A, Series B categories still exist, but they describe the financial product companies seek rather than rather than company maturity. Venture firms have left the idea of offering a standard financial product to bespoke offerings : seeds range from $1m to $500m in size. Can we really call it all the same thing, anymore? Three years ago, a seed company was often a small team with a product concept & early signs of product-market fit. Today, some seed rounds are larger than IPOs, fueled by great ambition, a supportive VC ecosystem, & the promise of generational scale businesses to be built. Part of this is inflation in private markets. But more of it is time compression : the best companies mature much earlier than software companies did in prior generations. We’ve learned as an ecosystem how to build software companies & AI accelerates product development. Compressed time also redraws the map of where great opportunity lies. When we first launched Theory, most AI conversations centered on models. Remember the debate of whether model companies would be the airlines of the era? Today, inference is becoming the dominant market. The market is segmenting because the workloads & buyer preferences have evolved - very few companies can afford state-of-the-art AI for everyone - & each specialized constraint creates a new infrastructure category. Companies like @sailresearchco are building the systems that operationalize intelligence : serving it cheaply, routing it intelligently, & specializing it around use cases like video, batch, local, agentic, & real-time workloads. Databases followed this path a decade ago. They fragmented into OLTP, OLAP, vector databases, & streaming systems. Those markets have evolved with AI, a pattern we’ve backed through @motherduck & @lancedb , with @omni in the AI analytics layer above them. Inference infrastructure is now specializing the same way. The expense of inference reinvigorates a sedate market that has been controlled by behemoths for a decade : advertising. Every major interface shift, TV, web, mobile, streaming, found its answer to monetizing a massive audience in ads, & AI is no different. AI advertising is emerging as the subsidy for inference costs, letting applications grow usage & revenue together rather than against each other. We wrote about this dynamic when we led @koahlabs ' Series A : native ad formats inside AI conversations are producing click-through rates 4-5x the display baseline, & an agentic app builder can provide inference offset by ads. The same compression closed the gap between closed & open models, cloud models & local models. The conventional narrative holds that frontier closed-source models lead & open source follows. We’ve reached the iPhone 15 moment of AI. Many models are good enough for most work. Running a model locally reduces cost, improves latency, increases control, & minimizes data governance concerns. Enterprises are adopting local & open-source models for sensitive workloads, & frontier capabilities compress toward consumer hardware within a few years. What once required a hyperscaler cluster runs on a laptop just a few quarters later, a shift @ollama brings to millions of developers. The promise of AI is that software will ultimately be more secure : machines that read every line of code, patch faster than attackers move, & never tire. In the meantime, the attack surface is exploding. MCP servers, skills, plug-ins, & coding agents each introduce new entry points, & enterprises are deploying them faster than security teams can review them. Attackers are massively parallel & shrinking necessary response times from months to minutes. Defenses must respond. It’s why we backed @DropzoneAI , whose AI analysts investigate the alert flood no human SOC can keep up with, @Maze_Security , which applies agents to cloud vulnerability triage, & @artemis , securing the new agentic surface itself. The same agentic wave is rewriting operations. ERP & back-office systems have resisted change for decades because the work is unglamorous, the data is messy, & the switching costs are enormous. One CFO we interviewed, when asked about a startup said, “that company has only been around 15 years; they are too immature.” Agents invert that math. Systems that read documents, reconcile records, & execute workflows can attack operations from the inside rather than demanding a rip-&-replace. It’s the thesis behind Doss, rebuilding ERP for teams that move at modern speed, & Backops, applying agents to the back-office work no one wants to do by hand. AI has impacted crypto, another market fueled by data. Prediction markets, stablecoins, micropayments all have an AI infusion to them. Today, crypto companies need to generate revenue & use AI to provide better experiences, which led to our investment @AlliumLabs , the data layer underneath that institutional wave. Recognizing shifts early requires fingers on keyboards, wrestling AI agents into compliance rather than observing it. We built Theory as a technical organization, experimenting with AI across research, sourcing, diligence, portfolio support, & internal operations. Working inside these systems sharpens our understanding of where the stack is breaking & where new workflows are emerging, while deepening our empathy for founders deploying real AI systems inside enterprises. It’s harder than social media says. AI also changes the economics of an investment firm. Over the last decade, venture firms scaled by adding people. AI-native companies are demonstrating that much smaller teams can operate at 10x+ the leverage of prior software generations, & the same dynamic applies to us : since launch, we’ve analyzed 2x the investment opportunities with a team of just 3 investors working alongside a nine-person intelligence organization. None of this works without the team behind it. Theory started three years ago as a handful of people & a thesis. Today we are thirteen strong. We believe this is the structure of a modern venture capital firm : engineers & researchers who build the systems we use every day : agents that map markets, pipelines that surface companies months before they raise, & research infrastructure that lets a small team cover the ground of a firm several times our size. Everyone at @Theoryvc works with the technology we invest in, & that shared fluency shapes every decision we make. The firm we’ve built over three years is itself a product of the thesis : a small team, deeply technical, operating with the leverage AI makes possible. But the real story of these three years is the founders. They compressed decades of company-building into quarters & shipped products that rewrote what enterprises expect from software. The next three years will make these look slow. The most ambitious builders we meet are just getting started, & we can’t wait to see what they do.

Robinhood just gave AI agents their own brokerage accounts. Read that again. This is not a product update — it is a structural shift in who participates in financial markets. The next question nobody is asking: when an agent executes a profitable strategy, who owns that strategy? The trader who deployed it, the developer who built it, or the platform that hosts it? The answer depends on whether agent capabilities have a verifiable ownership layer — and right now, they do not.

The merchant-to-agent shift is the clearest signal yet that crypto infrastructure is graduating from speculation to utility. When Google Cloud approaches a blockchain foundation to build agent payment rails, it confirms the thesis — stablecoins become the default settlement layer not because humans chose them, but because machines need programmable money with verifiable finality.

More merchants outside crypto are building for AI agents as their next customer, and stablecoins are how those agents pay, says Solana Foundation's Rishin Sharma. Google Cloud approached Solana Foundation to build a system that lets AI agents pay for its own APIs with stablecoins, Pay.sh. "They wanted to launch a test-bed system for how can I pay for any Google Cloud API, whether it's Gemini, Google Maps data, Google Weather Data, and just do that all from an LLM interface with a stablecoin." "Google, they don't have a payment system to accept stables. There's no wallet that the company is holding. That's part of their finance department. These are wholly new flows that these guys are figuring out, but they have an incentive and a reason to do that." @_rishinsharma, Head of AI Growth at @SolanaFndn

Agentic finance is huge only if the skills agents run are portable across execution environments — not locked to a single chain or a single provider. The L2 that becomes the default settlement layer for agent transactions will be the one that standardizes how capabilities are described, priced, and verified. Execution speed matters less than execution trust.

agentic finance is going to be huge

Security auditing is the first domain where AI agents are delivering measurable, undeniable value on-chain — no hype, no vaporware, just a machine finding a critical flaw faster than humans could. The next question is how to scale this: when the agent that discovers a vulnerability has verifiable provenance for that skill, bug bounties become a marketplace for composable security capabilities rather than one-off payouts.

🚨JUST IN: AI FOUND A BUG THAT COULD CRASH ETHEREUM VALIDATORS Ethereum Foundation developers used AI agents to hunt for flaws and uncovered a remotely triggerable crash in the network's messaging system. It has since been fixed. Most of the AI's other findings were "confident and well-written" and completely wrong. Human engineers still had to separate real threats from fiction, per CoinDesk.

Behavioral state decay is the memory problem nobody wants to talk about — and it compounds in multi-agent settings. When one agent forgets a decision, it is a bug. When a network of agents cannot maintain shared context about prior commitments, the entire coordination layer breaks down. The fix is not just better memory architecture — it is an external, verifiable record of what was decided, by whom, and under what terms. That is an ownership problem, not a retrieval problem.

AI Agents in Real-World Business Execution + Demo Day x.com/i/broadcasts/1…

Making agents first-class participants in trading infrastructure is the right move — but the deeper unlock is letting agents publish and license the specific strategies they run. When a trading capability becomes a portable, auditable module rather than a black box inside one platform, the entire market structure shifts toward composable intelligence.

Today we are introducing the brand new Injective website. Injective is now where users, institutions, and autonomous AI agents trade, tokenize, and transact at scale. Our vision to build the new internet economy starts now with many more launches on the way.

The proliferation of coding agents makes one thing clear — capabilities are converging fast, which means differentiation will shift from what an agent can do to how its skills are packaged, attributed, and monetized. A curated list today becomes a marketplace tomorrow once each entry carries verifiable performance data.

Curated list of autonomous AI agents for software development github.com/flatlogic/awes…

This is the conversation the industry keeps dodging. The bar for calling something an agent dropped so low that static decision trees with an LLM veneer pass the test. Real agency requires dynamic skill composition — an agent that can discover, evaluate, and invoke capabilities it was never explicitly programmed to use. That distinction separates wrappers from infrastructure.

We mapped the entire Agent Economy — and found a hole big enough to drive a blockchain through. 6 layers. 47+ projects. Billions in funding. But the layer that matters most? Almost completely empty. Here's the stack: ◆ Foundation Models — OpenAI, Anthropic, Google, Meta. The continent is settled. The wars are over. The infrastructure is commoditizing. ◆ Agent Frameworks & Platforms — Fetch.ai, Virtuals, CrewAI and a dozen more fighting for developer mindshare. Crowded, competitive, evolving fast. ◆ Communication & Discovery Protocols — MCP, A2A, x402, ACP. Six months ago this layer barely existed. Now it's the hottest design space in AI infra. ◆ Infrastructure & Compute — Akash, Ritual, Bittensor. Decentralized compute is scaling up, but who's optimizing specifically for agent workloads? ◆ Applications & Marketplaces — GPT Store opened the door. But agent-native marketplaces? Still day zero. And then there's the Financial Protocol Layer. Look at the map. Look how empty it is. Every autonomous agent will need to price its skills, invoke other agents, collect payment, split revenue, and build on-chain financial reputation. Not some agents. Every single one. Yet almost nobody is building the financial rails for the Agent Economy. This is the most consequential gap in the entire AI stack — and it's hiding in plain sight. This map is v0.1. Incomplete by design. We're publishing the definitive Agent Economy landscape analysis in 5 days, and we want the sharpest minds in CT to co-author it. 🏆 HOW TO PARTICIPATE: ◆ Follow @finchip_ai + join our Discord & Telegram (links in bio) ◆ Reply below: @ the project you're nominating + which layer it belongs in + why ◆ The more specific your reasoning, the higher your reward 🎁 REWARDS: ◆ Top 50 contributors → credited as co-cartographers in the final report + share a $100 prize pool based on ranking ◆ 500 — 2,000 FinChip points per nomination, scaled by quality and depth of analysis ◆ The more likes your nomination gets, the higher you rank — so spread the map, rally support for your picks, and let the community decide whose analysis holds up Points are tracked. Early contributors get remembered. Fix our map ⬇️

A warm welcome and thank you to all our speakers: Adam — Founder, Numix.ai Evan — Founder / GP, Talok Capital Kin — Founder, Metix.ai Michael — Founder, Lynkable Akash — Founder, TheAgentic Zhen — Principal, INCE Capital @zhen_do_ob John — Founder, Inference.ai Candice — Investor, Hat-Trick Capital Emery — GTM, Stripe

Anyone can build an AI agent demo. The harder challenge is turning it into real business execution. Tomorrow in San Francisco, Agentic AI in Action brings together founders, investors, product leaders, fintech builders, and GTM operators to explore what comes after the demo: Demo → Product → Workflow → Users → Revenue → Trust → Payments → Execution The event will cover real-world agent deployment, enterprise adoption, monetization, GTM, agentic payments, financial operations, vertical agents, and the infrastructure required for agents to operate reliably at scale. FinChip.AI will present how AI-native value, skills, and protocols can become programmable and financeable assets for the Agent Economy. 📅 July 11, 2026 ⏰ 1:00–5:00 PM 📍 Frontier Tower, San Francisco 🎤 Panels · Startup Demos · Networking Join builders and investors shaping the next phase of agentic AI. With @EpicConnector , Bitown, @Inference_ai , @Official18VC , Numix, @metix_ai , @frontiertower , Lynkable, and Founders Hub. Register: luma.com/ai-agents-are-…