In the span of just twelve months, Anthropic has shifted from being one of several frontier AI labs to a gravitational center of the industry. The change is driven by a compounding sequence of capital inflows, enterprise adoption, and infrastructure-scale positioning that increasingly resembles platform formation rather than startup growth.
The clearest signal came on May 28, 2026, when the company closed a $65 billion Series H round at a $965 billion post-money valuation, briefly making it the world’s most valuable AI startup ahead of OpenAI. Its valuation has climbed rapidly from $183 billion in Series F to $380 billion in Series G, and nearly doubled again in the latest round.
This momentum is being driven by strong enterprise demand. Anthropic now reports an annualized revenue run-rate above $47 billion, largely fueled by adoption of its Claude models in coding and agent-based workflows. Increasingly, Claude is being embedded into production systems where productivity gains translate directly into cost reduction.
Coding has become the primary growth engine, marking the second signal. Software development is now the operating layer of modern enterprises. As Claude moves deeper into these workflows, Anthropic’s identity shifts from product builder to infrastructure provider.
But rapid growth comes with pressure. The company is close to its first operating profit, yet compute costs remain heavy. In Q1 2026, it spent 71 cents for every dollar of revenue on compute, expected to improve to 56 cents in the next quarter. Efficiency is improving, but only because demand is rising fast enough to absorb training and inference costs. Yet Anthropic has also cautioned that planned infrastructure investments could make profitability difficult to sustain over the full year. This tension between scaling demand and managing compute costs is now a pressing challenge for frontier AI companies. Bankers and investors are increasingly focused on Anthropic’s token economics and compute costs, worried that rising AI usage costs could pressure margins and make it harder to justify its valuation after an IPO.
Which leads to the third signal: capital structure alignment. On June 1, Anthropic confidentially filed for an IPO, working with Morgan Stanley and Goldman Sachs, alongside J.P. Morgan Chase. Anthropic leadership notes that frontier model training requires sustained access to large-scale capital, and public markets are structurally better suited to that need.
Alongside expansion, Anthropic is also moving carefully on safety and control. Through Project Glasswing, the company has scaled access to its Mythos cybersecurity model from roughly 50 organizations to 150 across more than 15 countries. The system has already helped identify more than 10,000 high- or critical-severity vulnerabilities in widely used software.
The same capabilities used to detect vulnerabilities could also be used to exploit them, so the model distribution is limited to vetted partners. Expansion happens through controlled channels rather than open release.
Anthropic is also exploring broader deployment of the model through discussions with the EU cybersecurity agency ENISA, which could extend access beyond the US and UK for the first time – widening its user base through institutional gatekeepers.
What Anthropic is becoming
These shifts show Anthropic evolving into three roles:
A capital-scale company moving toward public-market size with trillion-dollar ambitions.
An embedded intelligence layer inside enterprise systems, especially in software development.
A controlled provider of high-risk AI systems, distributed through strict governance frameworks.
Anthropic is trying to scale and contain at the same time. The broader question is whether the economic and governance structures around frontier AI can scale at the same pace as the systems they are now trying to contain.
Introducing our new ‘Letter from the Editor’ series featuring exclusive insight and opinion-driven analysis from Tearsheet editor Sara Khairi.The focus is to link ideas, question assumptions, and track shifts across both mature and emerging trends in financial services.
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Issue # 3
For years, we’ve talked about digitization as if it were ‘the’ destination. We built apps, dashboards, APIs, embedded widgets, AI copilots. We optimized access, sped up onboarding, and compressed decision times.
Doing this made finance feel easier but not necessarily more present. It still shows up in bursts when you open an app, check a balance, apply for credit, or reconcile at the end of the month. The system is faster, but it remains episodic. You still go to it rather than it staying with you.
That model is starting to give way, and that is what I want to talk about today. Financial services are starting to move beyond the old request-response model. In its place is an incoming, always-present layer that interprets context in the background, responds dynamically, and participates alongside the user instead of merely waiting for input.
We’re already seeing the early contours of this across different parts of the stack. The recent Plaid-OpenAI integration around ChatGPT is one of them. On the face of it, it resembles another AI-powered personal finance assistant: users connect accounts through Plaid, and ChatGPT responds with contextual insights drawn from live financial data like budgeting support, spending analysis, debt management, savings recommendations.
Useful, sure. But also slightly too small as a way of describing what’s actually changing.
Historically, financial experiences lived inside financial products. What OpenAI is effectively testing is finance embedded inside a conversational intelligence layer people already inhabit constantly throughout their day.
That changes the center of gravity. The banking app is no longer the primary interface; conversation increasingly is. And conversation doesn’t behave like traditional software. It doesn’t reset every time you open it. It carries context, stretches across workflows, and stays present while decisions are forming.
This is why I think the industry narrative around “AI in finance” only captures part of what is happening and understates the shift underway; what is actually emerging is more like always-on financial interpretation.
And this evolution didn’t start with ChatGPT.
Embedded finance already moved things in this direction by pulling financial functionality closer to behavior. Shopify embedded capital and payments directly into commerce. Klarna and Affirm brought credit into discovery and intent, not just checkout. Banking capabilities stopped behaving like standalone destinations and started merging into workflows.
Emerging AI systems are what push that logic further.
Agentic AI in wealth and banking, payments and commerce
What’s taking shape now is embedded interpretation. Systems are increasingly expected not just to process transactions, but to understand patterns, maintain continuity across fragmented financial activity, surface relevance proactively, and eventually participate in decisions.
That is a much bigger transition than another chatbot layer. Previous fintech cycles optimized transactions; this one is beginning to optimize financial cognition itself. That changes the competitive landscape in ways I don’t think incumbents are fully prepared for yet.
Historically:
Banks owned accounts
Fintechs owned experiences
Now AI systems are positioning themselves to own interpretation
That third layer may become a highly valuable layer in financial services going forward. Because once a system becomes the place where users continuously interpret financial reality, every action – spending, saving, borrowing, investing, planning – flows through that layer.
This is why the Plaid-OpenAI partnership is gaining eyeballs, even if the product itself evolves, never fully scales as imagined, or struggles commercially. Some skepticism around the launch is warranted, though. Transaction data is incomplete, advice without execution still leaves friction, and consumer demand for AI-powered financial guidance does not necessarily mean they will pay for it at scale. Additionally, behavioral finance has historically been much harder than fintech companies assume or product demos suggest.
But those critiques mostly speak to product viability. The deeper shift is interface migration.
Finance is moving out of banking environments and into persistent intelligence systems that people already use to organize information, interpret decisions, and navigate daily life.
We can also see this in the way AI is being introduced into core banking and wealth workflows. Take the idea behind capabilities like Citi Sky.
Across these examples, AI isn’t acting as just an assistant added on top of finance but as a bridge or layer between raw activity and meaning. This is what distinguishes the current AI wave from everything that came before.
We’ve had digitization. Then automation. Then embedded finance. Each wave made finance more efficient, more distributed, and in some cases less visible. But this is about continuity. Continuity is not just availability, so to speak. It is context preserved over time, understanding what changed, what matters now, and what is likely to matter next, without requiring the user to rebuild the frame each time they interact with the system.
That’s a very different expectation to place on financial infrastructure. And it also reorders what ‘good’ actually looks like.
For years, the goal was to make finance invisible. API-first banking accelerated that by modularizing financial capabilities so they could appear anywhere. Embedded finance distributed those capabilities across commerce, payroll, and software ecosystems. Now AI introduces systems that continuously interpret financial context without being asked.
More intelligence does not automatically mean more clarity
A system can be highly responsive and still create noise. It can surface constant insights while still leaving users responsible for stitching meaning together. And in finance, that stitching has always been the user’s burden.
The ‘always-on’ narrative is often labeled as progress, but its real impact turns finance into an ambient layer.
That shows up in concrete ways in how systems begin to behave. A portfolio that doesn’t just report performance but contextualizes movement in relation to goals and macro conditions. A banking interface that doesn’t wait for queries but flags emerging patterns in cash flow or risk. A wealth tool that doesn’t just answer questions, but anticipates the framing of the question itself.
At this point, the line between ‘user action’ and ‘system interpretation’ starts to blur. And that is where incumbents face a harder challenge.
Financial institutions have always been strong at producing answers. What they are now being asked to build is continuity of understanding. Not correctness in moments, but relevance over time. That is a different operating model. And it is not yet clear that the industry is structurally set up for it.
There’s also a deeper question underneath all of this. If finance becomes continuously present – interpreting, explaining, and responding in real time – what happens to the moments where users used to pause, think, and decide?
Historically, friction was not always a flaw. Sometimes it was the point where attention was forced. A moment to pause, compare, reconsider. Remove too much of that, and you don’t just reduce friction; you potentially reduce visibility into the decision itself.
This is where the industry’s obsession with ‘seamlessness’ starts to feel questionable. Seamlessness feels effortless, but it is not neutral in effect.
This is not an argument against AI in financial services. It’s more of a reminder that presence changes behavior. Systems that are always available tend to become systems that are always shaping.
And that is the real design problem ahead: how much intelligence should stay in the foreground, and how much should disappear into the background until it is needed.
Because the endgame, at least as I see it, is not a constant stream of financial outputs, nor simply better UX or faster payments. It is about moving away from fragmented financial management and toward a system that understands a person’s financial life as it unfolds without overwhelming them.
– Sara
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Citi has launched an always-on, AI-powered member of its Wealth team that can engage in conversation, respond in real time, and surface insights instantly.
If AI becomes the front door to wealth management, banks don’t just face a UX problem; they face a structural one as well. How do you keep advice personal under regulatory constraint, and what happens to the human advisor when the interface is no longer human-mediated?
Citi is testing that question with Citi Sky, built with Google Cloud and Google DeepMind.
Citi Sky lets clients talk to it at any time. It delivers real-time market, portfolio, and opportunity insights, shifting wealth management away from scheduled interaction and toward continuous availability. Voice and multimodal interfaces, powered by DeepMind models, push it further into a conversational experience rather than a static dashboard.
But the guardrails are clear. Citi Sky does not execute trades. It interprets, explains, and prepares, while human advisors remain the final point of control.
Underneath that product positioning sits a harder engineering problem.
For Google DeepMind, the challenge is not building intelligence, but controlling it. Generative AI systems are inherently non-deterministic – the same input can produce different outputs, which makes consistency difficult in regulated financial environments.
JP Suh, Product Manager at Google DeepMind, says the fix lies in system design, which includes strict routing, Citi-specific tool use, and tightly bound context to ensure the agent operates within defined limits inside Citi’s environment.
Personalization is deliberately kept separate from model reasoning. Instead of being embedded in training, it is applied at runtime through controlled data access, a structure meant to preserve relevance while reducing hallucinations and keeping compliance intact.
What the move signals
Citi Sky signals that wealth management is moving from a pull model to a presence model.
Advice is no longer something clients ‘go to’ on a schedule. It becomes present in the background and is always available, always responding.
That compresses advisors’ surface area and changes their rhythm in the relationship. Routine interaction fades, replaced by fewer but higher-stakes moments where judgment, context, and trust actually matter.
Wealth management is slowly shifting toward a model where intelligence is continuous and human involvement becomes more selective, intentional, and contextual.
Wealth management follows a familiar rhythm where advisors book meetings in advance, send market notes after the fact, and make decisions that move at the speed of inboxes and calendars.
Citi Wealth is aiming to break that cadence with Citi Sky, built in partnership with Google Cloud and Google DeepMind. The bank describes it as an always-on AI-powered member of the Citi Wealth team that can talk, respond, and surface insights in real time.
Citi’s Head of Wealth, Andy Sieg, says the intent is to move away from the fragmented experience clients have lived with for years. “For decades, managing your financial life meant navigating apps, calls, and meetings,” he said in a press release. “With Citi Sky, you simply ask – and act. This is the shift from interface to intelligence, from transactions to outcomes.”
The Citi-Google Cloud relationship extends beyond a typical vendor arrangement. While Google provides the underlying infrastructure and AI stack, the collaboration evolved into a deeper co-development effort. Teams from Google Cloud and Google DeepMind worked alongside Citi engineers to shape Citi Sky’s architecture, conversational experience, and guardrails, while Citi retains ownership of the client experience, data, and decisioning layer.
From infrastructure modernization to client-facing intelligence
…
JP Suh, Product Manager at Google DeepMind
Karolina Belwal, Citi’s Global Head of Data Intelligence and Automation for Wealth
Rohit Bhat, Managing Director of Financial Services at Google Cloud
In January 2026, U.S. Bank announced the launch of a generative AI assistant on its developer portal to accelerate and improve partner API integrations. Just last week, the bank closed its deal for Amazon’s small-business credit card portfolio – viewed internally as both a portfolio expansion and a way to reach more SMBs. Around the same time, it also extended home-improvement loan terms in a calculated response to mounting affordability pressures.
These actions show how the Minneapolis-based lender is reorganizing itself around a methodical strategy focused on how quickly it can integrate, how intelligently it can respond, and how deeply it can embed itself in the systems where financial decisions are made.
In tandem, these moves form a closed-loop operating model where integration fuels usage, usage produces data, and that data perpetually refines products in near real time.
Breaking the Code: Turning integration into distribution
U.S. Bank rolled out its generative AI assistant for developers in October 2025, before formally surfacing it publicly in early 2026.
This launch is the clearest entry point into the bank’s systematic plan. On the surface, the tool solves a familiar problem: APIs are powerful but often complex, and integration can take weeks or months depending on the use case. By guiding developers through implementation, troubleshooting errors, and recommending best practices, the AI assistant materially reduces that friction. The bank says the AI assistant can reduce API integration timelines by an average of weeks, helping partners go live faster.
But the more important shift is not speed alone; it’s where distribution happens.
In traditional banking, distribution is relationship-driven: sales teams, partnerships, and channel expansion determine adoption. In an API economy, distribution shifts upstream. The bank that is easiest to integrate can become the one most likely to be embedded by default. In that context, the developer portal acts as the front door to U.S. Bank’s embedded finance strategy.
The relationship between clients and banks has been structured around a destination model, where businesses log in, navigate dashboards, export data, and piece together insights.
Grasshopper is working to dismantle that model.
In August 2025, the digital bank launched its Model Context Protocol (MCP) server in partnership with enterprise-grade digital banking solutions provider Narmi to address a specific challenge: enabling clients to use modern AI tools with their financial data without compromising banking security and control standards.
Nate Gruendemann, Director of Product at Grasshopper
“We learned people were uploading their bank statements or transaction files to their [external] AI of choice to run AI-analysis on their finances,” says Nate Gruendemann, Director of Product at Grasshopper. “MCP technology is how we close that gap.”
Technically, MCP sits between Grasshopper’s core banking systems and external AI models, managing authentication, permissions, and data structuring before any client-specific bank data reaches the AI model (e.g., Claude or ChatGPT).
“In practice, this allows us to expose meaningful financial context while keeping the core banking system insulated,” notes Gruendemann.
But the key design choice lies in what MCP doesn’t allow. The system is built on the assumption that AI models are untrusted environments. MCP is fully opt-in, which means clients must authorize Claude or ChatGPT and authenticate with their banking credentials. The server can see only the data the user is permitted to access, and the entire system is currently read-only. This means AI tools and platforms can analyze information, but cannot act on it independently. For example, they cannot initiate transactions or modify account data.
“We secure the banking infrastructure and access layer, while clients maintain control over how they use their chosen AI tools,” adds Gruendemann.
This indicates Grasshopper isn’t focused on owning the user experience, but on controlling the underlying data layer that powers it.
The rationale behind building a user-facing layer outside the core banking system
There’s a tension at the heart of modern banking that technology doesn’t seem to totally resolve: how do you be both digitally excellent and deeply human at the same time? Most banks have picked a lane: either betting on digital efficiency or doubling down on relationship banking. But consumers aren’t asking for one or the other. They want both. They want their banking app to work flawlessly when they need it, and they want someone who actually knows them when it matters.
My guest today is Dontá Wilson, Truist’s Chief Consumer and Small Business Banking Officer. He leads 20,000 teammates serving clients through both digital channels and more than 1,900 community banking branches. His portfolio spans core deposits and loans to mortgage, auto, credit cards, and the full stack of consumer products. He also oversees Truist’s multi-year growth plan that’s reimagining both their digital experience and their physical branches using insights and AI.
We talked about how AI is redefining consumer expectations and trust, what it takes to innovate inside a highly regulated industry while keeping client purpose at the center, and why Dontá believes innovation without empathy is empty.
We’ve been covering AI in financial services for a while now—chatbots, generative AI, fraud detection models. But something fundamental is shifting. We’re moving beyond AI as a tool that assists humans to AI as an actor that takes action on our behalf.
Agentic AI is no longer a research project. It’s live. Capital One has AI agents helping consumers buy cars. Visa is letting AI agents spend your money. RBC has agents executing trades, learning and adapting in real-time to market conditions.
It’s already here. The question is: what does it take to make this work at scale? What infrastructure do you need when an AI agent is handling real financial transactions at 2 AM? How do you architect for reliability when there’s no human in the loop?
My guest today is Kevin Levitt, who leads global business development for financial services at Nvidia. Before Nvidia, Kevin spent years inside fintechs like Credit Karma and Roostify. At Nvidia, he’s working with firms like Capital One, Visa, and RBC as they deploy agentic AI in production—not pilot programs, actual live systems processing real transactions.
We’re digging into the case studies, the computational demands of multi-agent systems, the security challenges when agents control money, and what financial institutions need to think about now.
NVIDIA’s Kevin Levitt is my guest today on the podcast.
Generative AI and open banking are beginning to change how banks engage with customers. Today we will look at this process with Olly Downs. He is a Chief Technology and AI Officer at Curinos. With a career spanning three waves of AI, Downs brings a wealth of experience to the table. He published his first academic paper on what we now call generative AI, back in 1999. “I’ve almost been waiting for the current wave of AI to join us,” Downs reflects. He highlights the long-anticipated arrival of today’s AI capabilities.
AI-driven personalization will change digital banking. Banks are beginning to use it to recreate the personalized touch of traditional banking. Downs explains, “Traditional banking founded itself on personalized, high-engagement relationships. That followed families and businesses throughout their entire life cycle.” Personalizing the online experience is challenging due to the growth of digital channels. Curinos’ technology tackles this by analyzing customer journeys. It identifies the best times and ways to engage customers. This ensures that personalization continues in the digital space. The result is a more effective and tailored customer experience.
Generative AI is not just boosting personalization. It addresses the entire marketing cycle for banks. This shift is redefining how banks approach customer engagement. It’s enabling and testing tailored interactions with numerous ready-to-use marketing creatives. The impact is both profound and widespread. The blend of personalization with open banking is shaping the future of banking.
1. Evolution of AI in Banking Personalization
Downs traces AI’s progress in banking, from Microsoft Research to today’s generative AI. He notes, “We’ve done so much better in understanding language. And the human internalization of concepts.” This progress has deepened our understanding of customer behavior across different communication channels. It provides a clearer picture of how customers interact, enabling banks to create more personalized experiences. Banks nowadays are focusing on data-driven customer lifecycle management.
2. Bridging the Gap Between Traditional and Digital Banking
Modern banks want to replicate the personalized touch of traditional banking online. This is a major challenge in the digital age. “The most satisfied retail banking customers engage with a branch. As well as digital services,” Downs says. This insight highlights the need for a consistent experience across all channels. AI helps unify customer journeys. It offers context for both digital and in-person interactions. Achieving this consistency is crucial for a seamless customer experience.
3. Generative AI: A Game-Changer for Financial Services Marketing
Generative AI addresses the marketing process for banks. Downs reveals, “We’ve been able to stitch in with the help of generative AI… how can we be experimenting live?” This technology allows for real-time learning and adaptation of marketing strategies. It accelerates the creative process and campaign execution.
4. Future of Open Banking and Personalization
Looking ahead, Downs contemplates the convergence of personalization and open banking. He muses, “There’s an opportunity for thinking about… pricing and packaging, both of deposit and lending products that can become very personal.” Yet, he also notes the potential challenges in data consolidation open banking might present, suggesting a need for consumer-driven solutions.
5. Micro-Personalization: The Next Frontier
The conversation touches on the concept of micro-personalization. It means “personalization for an audience of one.” The goal of personalized banking is to integrate both branch and digital services. Downs notes that open banking trends and data privacy issues make this complex. These challenges make personalization more difficult.
The Big Ideas
AI-driven personalization is reviving traditional banking relationships. Downs highlights, “Traditional banking founded itself on personalized, high-engagement relationships.” He explains how AI is enabling banks to maintain this level of personalization. It is doing this across digital channels.
Generative AI will change financial services marketing. Downs reveals, “It’s a massive unlock. It’s a hundred X unlock of the creative process in particular.” This technology allows for continuous experimentation and rapid adaptation of marketing strategies.
The future of banking lies in the convergence of personalization and open banking. Downs predicts a future where banking products are highly personalized, stating, “There’s an opportunity for thinking about… pricing and packaging, both of deposit and lending products that can become very personal.” Yet, he also acknowledges the challenges that it might present in data consolidation.
Customer engagement is key to long-term value. Downs explains, “The key use case has been about engagement and the path to primacy and maximizing quality of customers.”
AI is enabling real-time learning and adaptation. Downs describes how Curinos technology can “generate new recommended creatives”. It does so in that “flow for the marketing team.” This allows for the immediate implementation of insights gained from customer interactions.
