What does it take to scale AI with confidence in financial services? That's the conversation we're bringing to #Sibos—agents#, governance, and all. Whether you're joining us onsite or not, get our latest e-book:

A few weeks ago at Sibos I spent a lot of time with banks. Every conversation came back to the same three things. Stablecoins, tokenization, and 24/7. I sat down with Mastercard's leadership at Sibos, including their heads of stablecoins and commercial BD. Their fastest-growing business is virtual cards, and AI agents are a big reason why. Their view was that stablecoins come next. Mastercard and BVNK launched the Mastercard Global Money Platform. A day later, Coinbase and Marqeta expanded their partnership. Two of the biggest pipes in payments, in the same week. Total stablecoin supply is $305.2B. A year ago it was $304.3B. Basically flat. But usage hasn't been flat. Standard Chartered says onchain stablecoin volume is running near $10 trillion a month, with velocity roughly doubling. So the dollars that are already onchain are moving a lot more. The card rails are how the next batch shows up. At Sibos the honest answer I kept getting was that banks are tokenizing because they have to go 24/7. The execs want it, the clients want it, and tokenized deposits are the fastest route. Vibhu from Solana pointed out that Crypto trading volume "falls off a cliff after the market closes," even for assets that trade around the clock. A lot of that liquidity only shows up during TradFi hours. His guess is that once TradFi goes 24/7, total crypto volume does 3-5x. When the big balance sheets stop clocking out, onchain markets stop going quiet on weekends. Drake Evans from Agora put it simply on the show. Bots don't care about the user interface or onboarding. "They care about the price." He thinks competition gets pushed "away from marketing, user experience, everything else towards economics." That's why cards and stablecoins fit together so well right now. Cards are how agents pay today. Stablecoins are built for machine-to-machine and pay-per-use. And the revenue is real Hyperliquid has averaged about $2.05M in revenue a day over the last 90 days. Card networks are wiring in stablecoins. Banks are racing to 24/7. Agents are choosing whatever's cheapest. The venues already running nonstop are printing.

André Rezende at @CredicorpGroup shares why knowing how to direct AI effectively will be key to getting the most out of AI agents and reshaping how teams work. Full episode recorded in partnership with @Kyndryl at @Sibos Miami 2026:

Sophie Marnhier-Foy at @Nasdaq explains how agentic AI can improve capital markets processes while keeping human oversight, trust and responsible governance at the centre. 💡 Full episode recorded in partnership with @Kyndryl at @Sibos Miami 2026:

I gave Claude Code, Codex, and Cursor a self-improving, shared MEMORY. Now my coding agents learn from each other, and their knowledge follows me wherever I work. Coding agents work in silos. Your corrections in Cursor don't carry over to Claude Code, and skills built in Codex don't automatically transfer to other harnesses. So switching tools often means starting from scratch. What if every coding agent could learn from everything you've already done? I invited @jqdsouza, co-founder of Asymptote Labs, to show me how to make this possible with Beacon, a 100% open-source memory layer for coding agents. We set it up live, explored real coding sessions, and watched Beacon turn past work into reusable knowledge. Here's what I learned: 1. One memory layer for 20+ coding agents. Beacon captures activity across Claude Code, Cursor, Codex, and 20+ harnesses using OpenTelemetry, hooks, and local session files. Everything gets normalized into a single format, regardless of which agent you're using. 2. Your entire coding history becomes searchable. Every prompt, tool call, command, file change, and response is captured in a structured trace. Instead of losing valuable context when a session ends, you build a searchable history of everything your agents have done. 3. Switch agents without starting over. Hit your usage limit in Cursor? Open Claude Code and ask it to continue your latest Cursor session. Beacon retrieves the relevant context in milliseconds so your next agent can pick up where the last one left off. 4. Your agents learn from your past work. This is where things get interesting. Beacon identifies repeated workflows and turns them into reusable skills. In our demo, it discovered a recurring UI development workflow and generated a skill containing the exact dev server commands, screenshot steps, and deployment instructions. You teach one agent something once. Every other agent can benefit. 5. Stop accumulating skills nobody uses. Have 100 skills but only use five? Beacon analyzes actual usage across thousands of sessions and identifies skills worth keeping, combining, or removing. Your agent's memory gets more useful, not just bigger. 6. Security rules that follow you across every harness. Beacon includes hundreds of detection rules that work across agents. It can identify risky behavior, including sequences where an agent reads sensitive credentials and then makes an outbound network request. 7. It even caught a real credential leak. While reviewing Justin's actual Cursor history, Beacon surfaced an incident where part of a production database password had been sent to a model provider. That's the power of having visibility into what your agents are actually doing. 8. One prompt to get started. Completely open source. Paste a setup prompt into your coding agent and you're off. Keep everything local on your machine, or use Beacon Cloud for free. If you're juggling multiple coding agents, you need to check this out. Drop a like and share it with someone facing the same issue. Beacon on GitHub: (don't forget to star 🌟) Enjoy the episode. Cheers!

Introducing Stable Takes A new 20-minute weekly mini podcast from @sytaylor and me. Each week we'll discuss the top stories in stablecoins and agentic payments. Episode 1: SIBOS recap, OUSD launched, and agentic bank runs.

Hear me out: Agentic TPMs (Technical Program Managers.) I had this idea in Sydney while chatting with Martha McKeen at CBA. I think this winds up being the most immediate and direct way that coding agents can make their way into the enterprise, and it will set the stage for true AI employees rolling in next year. So. Build-side agents are great but they don't escape the SDLC. Only devs are using them. There are a handful of business people vibe coding SaaS, but for the most part, non-engineers aren't using coding agents to help with their jobs. Right? Not yet. Autonomous 24x7 unmanned queue-based "operator" agents, like the ones that handle internal or external customer issues, are great. But they are narrowly scoped, and generally require devs involved to set them up and maintain them. Neither builder nor operator agents are automatically going viral internally and helping run the company. They stay in their lanes. But what if their lane was to help run projects? I was a TPM at Amazon in 1999. Bezos brought in high-powered engineers with people skills to run difficult cross-functional projects and programs. TPMs are used at Google, Uber, Netflix, and other companies, and they are always in high demand and short supply. I have a class of agents in my Wheelhouse factory that act just like TPMs. They have external email and Slack, and talk to my accountant, lawyers, players. Each one has a project lane and drives it. They use Progress By Nagging, which... works. A TPM owns delivery, but has no authority, and no resources. They can only ask, observe, document, and report. This is just like my TPM Wheelhouse seats, who have been helping me drive dozens of projects to completion, large and small, for months. Agents, particularly smarter models, will go to great lengths to document the hell out of everything in the domain where they're operating. They'll capture all the tribal knowledge and unwritten rules. They can create topological maps of your project, org dependencies, and workflows. They'll bulldoze through silos and knowledge-hoarders and figure out how the company actually works, and document it all. And nag people along the way. This kind of agent sits well in constraint-space. They're cheap: You don't need to use the fanciest models; anyone with Opus or Sol access could have a TPM agent. And TPM agents have low risk and blast radius, because they cannot act. Unlike builder agents, which create new problems (like merge-queue and code-review bottlenecks), TPM agents simply shine a light on the org, and nudge things along. It doesn't matter what format they're recording their findings in. It could be Sanskrit and hieroglyphics. When it comes time to merge their findings with those of other TPM agents, it will all translate trivially into your company brain. Anyone in the company can stand up a TPM agent. It's like a personal chief of staff. There's no dependency on engineers. Everyone can do it; it doesn't even have to have a paced rollout. And there's no product to buy, no tech to install, maybe just a Skill you give people. Maybe you put a company wrapper on it. But it's just an agent that's playing the TPM role. TPM agents will wind up training human orgs on human-agent interactions. Humans start getting emails or DMs from agents, work-related, and will have to get comfortable replying and interacting. Companies can push the social side along without waiting for engineers to finish messing with the SDLC, which honestly will never finish. Other kinds of agents struggle at enterprises because they lack context. TPM agents will build that missing context as their exhaust, no joke; they've done it for my game without me even asking. TPM agents are the jungle explorers that will map out your organization, and you'll discover all sorts of fun stuff, like that you had 3 teams doing the same thing. TPM agents are a low-risk, high-impact way to start figuring out how AI can help you run your project, or organization. I'll write a blog post about this, but feel free to start now. Go! Just give me credit when you win big with this idea. And if you want my help, ping me on

ICP "cloud engines" launch trajectory: DFINITY will soon begin disbursing coupons with enough cycles to create and power an engine for 1 month+, unlocking in sequence: 1. Agentic infrastructure ⚡️ Quickly build secure, sovereign onchain production apps using Claude Code, Codex, Caffeine, ... 2. Open SaaS beta release ⚡️ Install AIware apps that can run any enterprise, as easily as installing phone apps... 3. Unlimited growth ⚡️ A new end-to-end, mainstream onchain cloud tech stack will come into being. *** What this means *** ICP cloud engines are a new development on the Internet Computer designed to allow it to play the role of a revolutionary new cloud tech stack for the AI era. Currently, the Internet Computer scales by combining internal subnets, which are akin to individual, high performance blockchains hosting secure serverless software called canisters (something like smart contracts, but much more powerful, which can be used to create huge onchain apps, which serve their web screens directly to users) Cloud engines allow enterprises to create their own subnets that belong exclusively to them, which they control and configure — while their canister software can still seamlessly interoperate with software on the main network, and engines run by others. An enterprise creates a cloud engine by choosing nodes from a marketplace, which the ICP protocol combines into a revolutionary serverless cloud platform for them, where the computation and data of their hosted apps and services is tamperproof (immune to infrastructure hacks) and always on (incredibly resilient), AI agents can build and update apps far more effectively than on traditional tech, and next generation AIware software can be created. Today, enterprises demand to be able to control where their sotware and data lives, and cloud engines make that possible with no loss of flexibility. Cloud engines can even be re-configured with new nodes, without interrupting the hosted apps and services — the ICP protocol just handles it, granting complete sovereignty and freedom from vendor lock-in. In advance of general release, cloud engines are already being used by governments and NGOs seeking to create sovereign cloud tech stacks and apps. On general release, they will be used in far more mainstream ways too. Here are the details of what's coming. *** Agentic Infrastructure *** In the future, and really already today, nearly all of our apps and services will be created by AI agents rather than humans. Further, next generation software is "AIware," which has AI deeply integrated on the inside, which allows it to provide superpowers relative to traditional software. Agents have different needs when they building things. They can write software orders of magnitude faster than humans, and we will demand they update our production apps and services at the speed of conversation. Functionality will be fluid and its shape will change to match what we want. On traditional tech stacks, updating apps and services is a necessarily laborious and risky process for three reasons: 1) a security flaw might be introduced, 2) a systems configuration change might break them, and 3) the way that data is migrated might cause some to be lost, often in subtle ways that aren't detected at the time of the upgrade. This means that AI agents can't just be let of the leash to build and update at the speed they can. ICP cloud engines solve this. Hosted software is tamperproof, so agents can update without needing security teams in the loop. Hosted software is serverless, and the data lives in the logic, so apps don't. need things like operating systems and databases configured to run, so agents can update apps without having sysadmin teams in the loop either. And, if Motoko 2.0, the world's first languge targeting agents is used as the backend language, its framework is able to detect when an app update by AI might cause data loss, and simply require the agent writing the code to try again, reducing potentially catastrophic data loss to a harmless retry. Crucially, apps built with Motoko can also put AI agents on the inside, so that subject to permissions, they can see the data graph in the persistent memory used by serverless software on ICP, and access its functions. This enables you to instruct agents to do things in the apps for you, such as analyzing data, or performing actions, with great flexibility and power. ICP cloud engines are agentic infrastructure that change the game — and it will be possible to use them as such from the moment they are released. There is already a government using internal production software built on a cloud engine using Caffeine. But cloud engines will meet a wide range of mainstream needs, from solorpeneurs to startups, SMEs to corporations, governments and NGOs. *** Open SaaS *** The obvious question, is if AIware is so much better, shouldn't all enterprises be running on it? We believe they should, and that his must be enabled. This has been a labor of love for me. A new venture associated with me will release the Open SaaS suite for use on cloud engines once they have been running a short while, which suite, as you've probably guessed from the name, is open source and free. Open SaaS is a suite of every important SaaS (Software-as-a-Service) app used to run enterprises around the world today, reimagined as onchain AIware that runs on ICP cloud engines. Because of the unique nature of the software that ICP hosts, installing and maintaining these sophisticated enterprise apps on a cloud engine is as easy as installing and managing a phone app. A small selection of the apps: Open Team, Open HR, Open Accounts, Open CRM, Open Projects, Open Email, Open Messaging, Open Signing, Open Knowledge, Op... etc. There are many more, and there will be surprises. The user experiences are beautiful, and easily compete with the big money SaaS screens many enterprises are used to. Moreover, the are linked to what I called a "software factory" driven by AI, and the more enterprises that use them and provide feedback, the better they will get, in realtime. Today's expensive SaaS silos make it hard to integrate and combine enterprise data, and to unleash the full power of AI. They are the opposite to Open SaaS. In the future, enterprise users will spend far more time in AI chat than on SaaS screens. That's the future that Open SaaS enables and unlocks. Enterprise users can even say to their Open SaaS suite, "make me more profitable," and it will have a go. The big surprise coming for the world: once you run your enterprise on AIware, and AI can access all your data in realtime, and is integrated with your software's actual functions — it can become as good at running your enterprise as writing software for you. It's revolutionary, and ICP is pioneering this. Not a narrative, but real decentralized network tech. Something I can share: DFINITY is in the process of migrating to Open SaaS from a vast suite of expensive SaaS silos, and is already reaping big benefits as we refocus ourselves for the era of AI. **** What does this mean for the project? I believe ICP cloud engines are the vector that will transform the the Internet Computer into a mainstream agentic infrastructure tech platform for enterprise— which direction platforms like Caffeine have already helped the network travel, by enabling many tens of thousands of users and enterprises to create onchain apps on the shared network through online conversation. Now, the next and most important phase is approaching. Our go-to-market will proceed quietly as we rollout coupons and work to fettle the new network functionality, but I think that in hindsight, the launch of cloud engines will be seen as the most pivotal moment in Internet Computer history since network launch. With this, ICP shall show the industry how networks can provide real agentic infrastructure. Bear with us as we tie up loose ends. We never stop working, and you can be sure that we're trying to get this out as fast as possible 💪⚡️💥

Intel CEO Lip-Bu Tan sat down with CNBC’s Jim Cramer to talk about Apple; Intel 14A rivaling TSMC's top chip production technology; Shortages of CPUs and substrates; and the state of Intel's turnaround. How to know when Intel signs Apple or other foundry customers: Intel CEO Lip-Bu Tan: “Over time, the IP will be ready so we can serve some of these customers. I think the best indication, when you see I increase my capex, I’m putting money to buy equipment, that means I have real customers. That’s the discipline I have.” Intel 14A manufacturing process and EMIB-T advanced packaging: Intel CEO: “(A14) is 1.4nm, this is the most advanced. To be candid with you, in 2028 we will have risk production. 2029 will be volume production. It will be the same time as TSMC. So that is a major, major breakthrough, and I’m so excited. And we already have multiple customers engaged with us, and we have 0.5 PDK available.” Taking advantage of TSMC’s CoWoS shortage Intel CEO: “Our technology is called EMIB-T, this is the next generation of advanced packaging. We really have the best technology and now we are making sure we can bring it into volume production with reliable yield so the customer can count on us.” CEO: “You know, CoWoS…(TSMC) ran out of capacity, so in a way we’ve become in the unique position to support that and that’s something we are very excited (about).” Shortages of CPUs and Substrates CEO on the CPU shortage: “I’ll give you one example. I had one customer say Lip Bu, we gave you the forecast for this year, but we want to increase 3x, and I say I cannot do it overnight but give me a few quarters and I will catch up. So, I think this demand is not short term, it’s the next couple of years. It’s a great opportunity.” CEO: “Right now, as I mentioned, CPU is in high demand. And that’s good for me. I cannot even ship enough to the customer. It used to be the CPU to GPU ratio for training was 1-to-8. And now, because of inference and agentic AI, and more agents you have to manage, and orchestration, and reinforced learning, CPU is actually better, so that becomes 1-to-4 and 1-to-1 and some people even talk about 4-to1, and so that’s a huge opportunity to me to drive the CPU....” CEO on Substrates: “A couple of customers have prepaid for substrates, because the substrate supply chain is very tight – so I need to put up the money to secure this material…(and it shows) the commitment to me – so that’s very exciting.” Intel’s Turnaround CEO: “We used to have leadership in data center, and over the years we lost it…We made some big mistakes,” he said, adding he’s brought back some talent to refocus the product lines and that “Coral Rapids will have multi-threading, and will come out very strong.” CEO: “When I took over, the 18A yield was not good, so I had to ask some of the ecosystem partners to help me look at the data, see how to improve. The best practice is to see 7% or 8% yield improvement per month, and now I’m seeing it.” CEO: “The other part is supposed to be the yield performance, defect density, you know at the end of the year to see the target. Now I see that even before the end of the year – so that is very big encouragement for me and also that’s why Panther Lake can be shipped in volume now. And now some customers knock on my door and say Lip Bu, now we hear you are making great progress, can you now open up to outside customers? So that is very exciting. It’s a lot of hard work, it’s a lot of teamwork, it’s a lot of talent I’ve brought on board.” CEO: “In the past we made a lot of mistakes and now we correct the mistake and we’ve simplified the roadmap. By the way, from Day 1 I came on board as the CEO, I have all the engineers report to me so I have an understanding, hear from the customer, and know where are the mistakes.” Cramer: “They didn’t report to the previous CEO?” CEO: “No. And in a way, they had too many silos, too many people reporting…So I decided, the best thing is to really understand where the problem is, so I can focus on the engineering, how to redesign, simplify the product and then get the real killer products out.” Cramer asks about China, Taiwan and the importance of US manufacturing: CEO: “I was very glad for President Trump understanding the strategic importance for the United States to have (chip manufacturing supply chain) and their support is so valuable to me – it’s so critical for the country to have the technology, R&D development, manufacturing in the United States. That’s why I came back in, as a U.S. citizen – as a calling – to do that.” CEO: “From time to time I update President Trump and also (Sec.) Howard Lutnick and they are big supporters of me and we are delighted to have their support.” Going forward: CEO: “I recruited some key talent…and now, by the end of June, I will have my team, what I consider my team, so that we can work on the next 5-years, 10-years, how to become a different company. I call it the New Intel, work at the speed of light, work as a team to progress forward.” $INTC $TSM #Samsung# $UMC $GFS #semiconductors#

What happens when a global manufacturing leader builds an "Agent Factory"? Leonhard Kerscher and Marina Kunert share how Kärcher uses Google Workspace and Gemini Enterprise to break down silos and shift employee focus from gathering data to making decisions. Watch how they made it happen →