The Week in Market Moves | May 21-28, 2026


Company signals and market response

This analysis tracks the top company developments and how markets absorbed them through Thursday’s close, focusing on where shifting narratives translate into price action.

It is part of Tearsheet PRO’s weekly 10-Q Newsletter, where strategy meets market reaction. I track how leading banks and fintechs are evolving in public markets and how investors are pricing those moves.

Subscribe to PRO and get the full 10-Q story every Friday!




1. Mastercard (MA) – Close: $493.75

  • Mastercard is asking Brazilian processors to share half the losses tied to Banco Master’s collapse and Will Financeira’s card portfolio exposure.
  • The dispute sits at the intersection of new central bank liability rules and legacy card-network risk allocation during issuer failure.

Why it matters: Mastercard is testing how far network liability can extend when an issuer fails mid-transition in a tightening regulatory regime. Banco Master is a Brazilian bank that grew rapidly through high-yield debt funding and later faced cash flow stress, leading to its collapse and liquidation. The Banco Master collapse exposed ambiguity over who absorbs systemic fallout in card ecosystems.

Brazil’s central bank has already shifted more responsibility onto payment networks for settlement guarantees, but Mastercard is pushing back on retroactive interpretation of those rules. The standoff signals a broader fault line: as regulators push for guaranteed settlement finality, networks are being forced to rethink how risk is distributed across issuers, acquirers, and schemes.

If unresolved, this becomes less about one failed fintech and more about how payment networks price and structure systemic risk in emerging markets.

2. Circle (CRCL) – Close: $108.24

  • Circle co-founder Sean Neville’s Catena Labs raised $30M and received OCC acceptance for a national trust bank charter application.
  • The company is building an “AI-native financial institution” designed for agent-driven transactions with embedded controls and policy layers.

Why it matters: Circle co-founder Sean Neville is now rebuilding the financial stack around AI agents. His new venture, Catena Labs, is an AI-native financial infrastructure startup positioning agents as the primary actors in moving money, with humans acting as supervisors rather than initiators. This extends his earlier work in stablecoin-based payments into regulated banking rails designed for agent-driven finance.

The key shift is architectural: agents get wallets, balances, and payment rails, while humans get a “control plane” to set constraints, approvals, and limits. That separation signals where the industry is heading, away from human-initiated transactions and toward delegated economic activity executed by software.

If this model scales, the core battleground in financial services shifts from UX and apps to governance infrastructure: how much autonomy AI agents are allowed to have, and who controls the boundaries of that autonomy.

3. Robinhood (HOOD) – Close: $84.84

  • Robinhood received Canadian regulatory approval for its acquisition of WonderFi, deepening its crypto infrastructure footprint.
  • The deal complements earlier acquisitions like Bitstamp as Robinhood expands custody, compliance, and trading infrastructure.

Why it matters: Robinhood is rebuilding itself as a multi-layer financial platform spanning brokerage, crypto infrastructure, and emerging market-style financial products.

Crypto trading revenue has fallen sharply, down roughly 47% year-over-year, but that decline is being offset by subscription products, prediction markets, and derivatives-linked activity. The WonderFi acquisition announced in 2024 extends this shift by adding regulated Canadian crypto rails, staking, and custody capabilities.

The broader signal is structural repositioning: Robinhood is moving from a single-product brokerage dependent on trading volatility to a platform that combines investing, speculation, and infrastructure ownership. In that model, trading becomes one of several monetization layers inside a broader financial ecosystem.

May’s public fintech theme: Operating systems over products


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    May’s public fintech theme: Operating systems over products

    Firms are pushing closer to the decision layer where financial actions are executed.


    May showed that companies are tightening control over the infrastructure that runs financial systems, including coordination, decisioning, workflows, and the underlying data layers.

    Across Coinbase, LendingClub, Green Dot, Citi, and Intuit, the details differ. The direction doesn’t. Each firm is moving one layer down the stack, closer to where financial decisions are actually made and executed.

    1. Coinbase: Still trading-led, increasingly infrastructure-shaped

    Coinbase is building toward an “everything exchange,” but its business is still defined in real time by trading.

    Q4 2025 (reported February 2026) made that clear: revenue of $1.78B, down 22% year over year, and a $666M net loss tied to weaker trading activity. Yet subscription and services revenue held up at $727M, driven by custody, stablecoins, and institutional products.

    Two engines are now visible:

    • Trading: volatility, upside/downside driver
    • Subscriptions and infrastructure: baseline, recurring layer

    CEO Brian Armstrong called Coinbase in “pole position” for 2026. Structurally, it is still a volatility-priced company building a stability engine underneath it.

    2. LendingClub: The business changed first, the name followed


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    The uneven geography of modern finance: AI, branches, and BNPL

      Financial firms are choosing to anchor themselves before, during, or after a transaction.


      This week didn’t offer a single storyline to hang everything on. Instead, it gave us three companies moving in completely different directions, signaling how far financial services has drifted from any shared playbook.

      PayPal is trying to teach small businesses how to use AI. Chase is opening more branches in an era defined by digital banking. And Klarna is turning payments into more of a daily habit loop.

      It all comes down to: how do you stay relevant when the way people interact with money is shifting faster than the institutions themselves?

      PayPal wants AI to stop feeling like AI

      PayPal and Anthropic have partnered to help small businesses adopt AI, including training and workflow integration. 

      PayPal is no longer confined to the checkout flow; through its integration with Anthropic’s Claude, it is moving closer to SMB workflows where transactions are initiated and executed. In effect, it is extending from a payments layer into the operational layer around business activity.


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      The Week in Market Moves | May 14–21, 2026


      Company signals and market response

      This analysis tracks the top 5 company developments and how markets absorbed them through Thursday’s close, focusing on where shifting narratives translate into price action.

      It is part of Tearsheet PRO’s weekly 10-Q Newsletter, where strategy meets market reaction. I track how leading banks and fintechs are evolving in public markets and how investors are pricing those moves.

      Subscribe to PRO and get the full 10-Q story every Friday!




      1. Klarna (KLAR) – Close: $15.93

      • Klarna is embedding itself directly into Worldline’s acquiring and merchant infrastructure, starting with e-commerce and eventually extending into in-store POS systems across Europe.
      • The partnership gives Klarna distribution through one of Europe’s largest payment acceptance networks at a time when BNPL usage is expanding beyond discretionary purchases into everyday cash flow management.
      • Klarna launched a shopping app inside ChatGPT that lets users search products, compare prices and check live inventory without leaving the conversation.
      • The move pushes Klarna upstream from payments into product discovery, where purchase decisions increasingly begin inside AI interfaces rather than search engines or retailer apps.

      Why it matters: This move is less about adding another payment button and more about distribution power. Klarna is moving from being a checkout feature into core payments infrastructure. By integrating deeper into Worldline’s stack, Klarna becomes easier for merchants to activate and harder to ignore. The timing matters too. BNPL is increasingly functioning as a short-term cash flow tool for younger consumers. Klarna understands that the winner in BNPL may not be the firm with the best consumer app, but the one most deeply wired into merchant systems and transaction flows.

      Klarna is positioning itself for a world where commerce starts with conversation instead of browsing. If consumers begin asking AI what to buy, the firms controlling that discovery layer gain influence long before checkout happens. What’s interesting is that Klarna is no longer waiting at the payment stage. It wants to sit at the moment of intent formation, when shoppers compare, evaluate, and narrow choices. That changes Klarna’s role from transaction processor to commerce intermediary. In AI-driven retail, discovery may become as valuable as payments themselves.

      2. Intuit (INTU) – Close: $307.07

      • Intuit is cutting roughly 17% of its workforce while redirecting resources toward generative AI infrastructure and product integration.
      • The company is simultaneously restructuring around AI-powered services rather than standalone software tools, supported by partnerships with Anthropic and OpenAI.

      Why it matters: This is not just a cost-cutting story. It reflects a deeper shift in how software companies think about value creation. Traditional SaaS products were built around menus, workflows, and manual inputs. AI changes that model entirely.

      Intuit envisions that accounting, tax, and SMB operations will increasingly run through AI-led orchestration rather than conventional software navigation. The layoffs signal how aggressively firms are willing to reorganize themselves around that assumption even before the long-term economics are fully proven.

      3. NVIDIA (NVDA) – Close: $219.51

      • NVIDIA reported a record $82 billion in revenue for the first quarter of FY2027, which corresponds to the quarter ending around April 2026, as demand surged for infrastructure powering agentic AI systems capable of executing tasks. 
      • The company introduced a clearer distinction between AI reasoning infrastructure and AI execution infrastructure, positioning new chips like Vera around task completion economics rather than raw compute rental.

      Why it matters: The important shift here is that AI infrastructure is becoming tied to staffing replacement economics. Earlier AI waves mostly enhanced software features. Agentic AI is being sold as operational capacity; systems that can investigate, execute, and complete workflows with minimal human involvement.

      That changes the spending logic. Businesses no longer view AI compute as experimental R&D spending. They increasingly view it as infrastructure directly tied to productivity and cost savings. NVIDIA is effectively becoming the industrial backbone for automated digital staffing.

      4. American Express (AXP) – Close: $309.70

      • American Express and Fanatics are launching a co-branded sports rewards card tied to FanCash, collectibles, tickets, and fan experiences.
      • Fanatics will also become Amex’s first sports-focused Membership Rewards transfer partner, linking payments directly into a broader sports commerce ecosystem.

      Why it matters: This deal shows how rewards programs are evolving from generic cashback structures into identity-driven ecosystems. Sports fandom already produces recurring spending behavior, emotional loyalty, and community participation. Payments firms increasingly want to sit inside those engagement loops.

      For Amex, the card is less about transactions alone and more about relevance. The goal is to turn spending into participation, where rewards are connected to experiences, access and belonging rather than points accumulation. Fanatics, meanwhile, gets another mechanism to keep users circulating inside its ecosystem longer.

      5. J.P. Morgan Chase (JPM) – Close: $303

      • Jamie Dimon said J.P. Morgan could eventually hire more AI specialists than traditional bankers as automation reshapes parts of the bank.
      • The firm is already deploying AI tools across investment banking workflows and cybersecurity operations while relying on natural attrition to gradually rebalance its workforce.

      Why it matters: Banks are moving beyond experimenting with AI and starting to redesign organizational structures around it. What J.P. Morgan is describing is not simply productivity software layered onto existing jobs. It is a gradual reallocation of staff toward technical and AI-operational roles.

      Dimon’s vision is notable because it is more pragmatic than the “AI replaces everyone” rhetoric circulating elsewhere in banking. But the direction is still clear: future financial institutions may compete less on headcount scale and more on how effectively they combine domain expertise with machine-driven execution.

      The Week in Market Moves | May 7–14, 2026


      Company signals and market response

      This analysis tracks the top 5 company developments and how markets absorbed them through Thursday’s close, focusing on where shifting narratives translate into price action.

      It is part of Tearsheet PRO’s weekly 10-Q Newsletter, where strategy meets market reaction. I track how leading banks and fintechs are evolving in public markets and how investors are pricing those moves.

      Subscribe to PRO and get the full 10-Q story every Friday!




      1. SoFi (SOFI) – Close: $15.77

      • SoFi has acquired PrimaryBid’s directed share program assets, ending its independent operations.
      • The deal builds on an existing partnership around equity issuance infrastructure (DSP2.0) for public offerings.

      Why it matters: This is SoFi pushing beyond consumer banking into the plumbing of capital markets. Instead of just letting users invest, it is starting to touch how equity offerings are structured and distributed. The move signals a broader ambition: not just to serve retail investors, but to sit closer to how companies raise capital in the first place. This shifts SoFi into a more complex, institution-facing layer of finance where scale alone is not the advantage; equally important are trust, regulatory depth, and execution quality.

      2. Remitly (RELY) – Close: $23.23

      • Remitly Business is now fully available to SMBs in Canada after earlier US and UK expansion.
      • New features include bulk international payments and ‘send via link’ workflows to simplify recipient onboarding.

      Why it matters: Remitly is trying to turn cross-border payments from a consumer remittance product into a business operating layer. The real opportunity is removing the operational drag of international pay runs, beneficiary errors, and fragmented payout workflows. If it works, Remitly becomes less of a remittance app and more of a back-office utility for global SMB commerce. The challenge will be whether it can stay simple as it moves deeper into business complexity.

      3. Coinbase (COIN) – Close: $220.70

      • Coinbase introduced Solana-backed loans, allowing users to borrow USDC against SOL holdings.
      • Loans are powered by Morpho’s on-chain lending infrastructure with instant access and flexible repayment.

      Why it matters: Coinbase is continuing its shift from exchange to financial infrastructure layer. Crypto-backed lending turns dormant assets into usable cash flow without forcing users to exit positions. That pushes Coinbase closer to being a credit intermediary, not just a trading venue. The bet is clear: if crypto assets are going to sit in long-term portfolios, the real value comes from making them productive. The risk sits in volatility cycles as credit tied to asset prices is only as stable as the market beneath it.

      4. Affirm (AFRM) – Close: $66.16

      • Transaction frequency per user has risen 50%, reaching 6.7 transactions annually.
      • Affirm is expanding into cards, wallets, banking integrations, and an industrial bank structure.

      Why it matters: Affirm is moving away from being a point-of-sale financing tool and toward a broader payments network. The emphasis is no longer just lending at checkout, but increasing how often users interact with the ecosystem. That creates a feedback loop: more transactions generate more data, which strengthens underwriting and improves targeting. The ambition is to become embedded in consumer spend flows.

      5. Chase (JPM) – Close: $300.33

      • J.P. Morgan is preparing to launch its retail banking push in Germany, starting with a savings account.
      • The strategy mirrors an entry-first model used by digital banks: land deposits, then expand products.

      Why it matters: This is a conservative but deliberate entry into European retail banking. Instead of trying to compete head-on with incumbents, J.P. Morgan is using savings as a low-friction entry point to build customer relationships. It reflects a broader truth in retail banking: distribution often starts simple, then deepens over time. The harder question is whether a US banking giant can translate brand strength into everyday consumer relevance in a market where local incumbents already dominate trust and habit.

      BNPL moves into the conversation layer of commerce

        Affirm and Klarna embed BNPL directly into Google’s Gemini as shopping shifts from search bars to conversations.


        The checkout button is starting to lose its original place in the buying process as payments move upstream into AI interfaces.

        That shift is now surfacing inside Google’s ecosystem, where BNPL firms Affirm and Klarna are embedding installment payments into Google Search and the Gemini app through Google Pay. The integrations move BNPL directly into AI-driven shopping experiences, where discovery, comparison, and purchasing happen within a single conversational interaction rather than across separate search funnels and checkout pages.

        Affirm is designing BNPL for machine-readable commerce

        Affirm’s move into Gemini reflects how the company sees shopping and lending evolving together inside AI platforms.


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        The Week in Market Moves


        Company signals and market response

        This analysis tracks notable company developments and how markets absorbed them through Thursday’s close, focusing on where shifting narratives translate into price action.

        It is part of Tearsheet PRO’s weekly 10-Q Newsletter, where strategy meets market reaction. I track how leading banks and fintechs are evolving in public markets and how investors are pricing those moves.

        Subscribe to PRO and get the full 10-Q story every Friday!




        1. Chime (CHYM) – Close: $19.28

        • Chime posted its first GAAP-profitable quarter, Q1 2026, as a public company, while active members climbed to 10.2 million.
        • The company is leaning harder into higher-margin products like earned wage access, instant loans, and premium banking tiers.

        Why it matters: This feels like a transition point for consumer fintech. Chime is no longer operating like a challenger bank trying to acquire users at all costs; it is starting to behave like a full-stack financial institution optimized for monetization and retention. The tension is that scale changes expectations. Once fintechs move upmarket and deepen product exposure, they inherit the same scrutiny around trust, cybersecurity, and responsible growth that traditional banks have spent decades managing.

        2. Robinhood (HOOD) – Close: $76.31

        • Robinhood’s private markets fund has attracted 150,000 retail investors as of May 2026.
        • The company is pushing to give everyday investors access to high-growth private firms long before IPOs.

        Why it matters: Robinhood is trying to break one of the clearest structural divides in finance: private market access. For years, the biggest gains from companies like OpenAI or Stripe accrued largely before public investors could participate. Robinhood sees an opening in turning venture-style exposure into a retail product. That could reshape expectations around who gets access to wealth creation, though it also introduces a more complicated conversation around risk, liquidity, and whether retail investors fully understand what they are buying into.

        3. Intuit (INTU) – Close: $407.97

        • Intuit launched an AI-powered human capital management platform aimed at SMBs.
        • The company is combining agentic AI with human advisers to automate payroll, hiring, compliance, and workforce operations.

        Why it matters: This is part of a larger race to become the operating system for small businesses. Intuit already owns critical financial workflows through QuickBooks; now it is moving deeper into labor and workforce management, where SMBs still juggle fragmented software stacks. The broader outlook is that AI will collapse multiple operational layers into a single system. If that works, software vendors stop selling tools and start managing decisions.

        4. American Express (AXP) – Close: $317.40

        • American Express launched AI training and scholarship programs for small businesses and workers.
        • The initiative focuses on practical day-to-day AI adoption.

        Why it matters: A lot of companies are talking about AI as a technology shift. Amex is treating it more like a workforce shift. Small businesses are increasingly less worried about whether AI exists and more concerned with whether their teams know how to use it productively. By positioning itself around education and enablement, Amex is trying to stay embedded in the operational layer of small business growth rather than remaining just a payments and credit provider.

        5. Chase (JPM) – Close: $307.50

        • Chase rolled out revamped banking and credit products aimed at Gen Z and first-time banking customers.
        • The bank paired app redesigns with branch expansion and financial education initiatives.

        Why it matters: Traditional banks spent years assuming digital convenience alone would win younger customers. Chase is leaning on the fact that Gen Z wants a more hybrid arrangement: strong digital tools backed by physical access and guidance when financial decisions become more complicated. The deeper competitive shift here is that banks and fintechs are converging toward the same middle ground – modern UX, embedded education, and relationship-driven engagement – rather than competing on ‘digital versus physical’ alone.

        Green Dot and the case to make financial experiences feel calmer

          Green Dot is looking inward, toward the overlooked moments in product conversations.


          Money doesn’t usually create confusion at the point of action. It creates confusion in the pause that follows: when something has technically been done, but not yet fully understood. A transfer completes, a balance updates, a transaction clears, and still there’s a moment of recalibration, as if the system and the user are briefly out of sync.

          Most of fintech’s progress has been built around removing that first layer of effort by introducing fewer steps, faster rails, and cleaner interfaces. And it has worked – money today moves with a speed that would have felt improbable a decade ago. But what hasn’t kept pace is the emotional side of that experience: the need to feel certain about what those movements actually mean in real time.

          That’s the layer Green Dot is now trying to address more directly. Chief Product Officer Melissa Douros calls it “Cortisol UX” – a way of thinking about financial design that starts from the simple premise that users are often already stressed when they arrive. The product, then, is not just an interface for action, but a system that either amplifies or absorbs that stress.

          That’s the conversation with Green Dot’s CPO, Melissa Douros, and what it reveals about how financial products are evolving when clarity becomes the real measure of design.

          Melissa Douros, Chief Product Officer at Green Dot


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          Micro Case Study: How American Express is underwriting AI agent error to unlock trust in $trillion-scale agentic commerce

          The big question

          If AI agents can execute payments, what guarantees that they execute the right payments?

          The move

          American Express has launched Agentic Commerce Experiences (ACE) Developer Kit, which formalizes and verifies user intent before any transaction takes place. The firm has also introduced what it calls an “industry-first” protection against AI agent error, agreeing to cover eligible transactions when an agent executes an authorized but unintended purchase.

          For example, a user asks an AI agent to book a “quiet hotel room under $250”; the agent finds a deal and completes the booking, but if it’s next to a busy street, the transaction is valid yet misaligned with intent.

          How it works

          The new ACE Kit shifts payments from simple authorization to intent-driven execution:

          • User intent is captured as a structured, verifiable, and enforceable input.
          • That intent is authenticated and tied to tokenized credentials before any transaction is initiated.
          • Agents can transact on behalf of card members only within clearly defined, authenticated intent and control layers.
          • Amex extends purchase protection into agent-executed transactions.

          Instead of resolving disputes after the transaction, the system aims to reduce ambiguity before execution.

          “The model includes card member enrollment and authentication and gives card members the ability to manage controls directly in the Amex app – using structured intent, spend limits, merchant preferences, and tokenized credentials so the agent can only act within clearly defined boundaries set by the card member,” noted Luke Gebb, EVP and Head of Global Innovation at American Express. 

          “The core of our approach is that intent is not treated as a loose instruction – it’s treated as a structured, verifiable representation of Card Member intent that the system can evaluate and enforce,” he added.

          How is this different?

          Traditional payment systems answer one question: Was this transaction authorized?

          Agentic commerce introduces a harder one: Did this transaction reflect what the user actually meant?

          That gap between execution and intent is emerging as one of the weakest links in AI-driven commerce.

          McKinsey estimates that agentic and AI-driven commerce could generate trillions of dollars in economic impact by the end of the decade, but only if trust in automated execution scales alongside it.

          Amex’s move directly targets that trust layer. Its closed-loop network provides end-to-end visibility across users, agents, credentials, and transactions, allowing it to link intent, execution, and liability within a single system.

          Why it matters

          By underwriting agent error, Amex is enabling AI-driven payments, but also pricing and absorbing a new category of risk.

          That changes the equation for adoption. Agentic commerce won’t scale simply because agents can transact; it can scale when users trust that those transactions are executed correctly.

          In the Chart: Amex’s take on securing the agentic commerce stack

          Financial brands have an AI voice problem

          There’s a tell. Most people in financial communications know it when they see it, even if they haven’t named it yet. It’s not a single word, though “landscape,” “navigate,” “unlock,” and “harness” are doing a lot of heavy lifting right now. It’s more of a feeling: the sense that a piece of content was assembled rather than written with paragraphs of near-identical length, confident-sounding contrasts that don’t actually contrast anything, and a structure that builds toward a dramatic ending that ultimately goes nowhere.

          “It’s not that it’s wrong,” says Ashley Jones, Head of Financial Narrative. “It’s just that it’s not actually thinking.”

          Financial brands are caught in a trap of their own making. AI has made it cheaper and faster than ever to produce content. It has also made it harder than ever to sound like a person or a company, with a distinctive and differentiated point of view. The brands navigating this best have figured something out: AI can accelerate the work, but it cannot generate the thinking. In financial services, where trust is the product, the difference between those two things is everything.

          The skeleton in the room

          Ask any editor or communications professional who regularly reviews contributed content, and they’ll describe the same ghost. A broad framing statement. Three supporting points that don’t quite complete the framing. A conclusion that restates the introduction. And nowhere in the piece — not once — is there a sense of a human being pushing against something, working through a counterargument, or saying something they genuinely believe.

          Anna Kragie, Senior Director at The Fletcher Group, describes the tell as structural more than stylistic. AI-generated content, she says, leans on empty claims and broad statements that could apply to any company in any category. Financial Narrative’s Jones puts it more bluntly: there’s no counterargument — nothing someone noticed was happening that the data finally confirms. 

          Michael Marinello, Global Head of Communications at J.P. Morgan Payments, points to a recent Barron’s piece on how AI is reshaping corporate content, including sentence structure, as evidence that the pattern is now visible enough to be written about in the financial press. When the tells are getting covered in Barron’s, they’re no longer subtle.

          Journalists and editors are catching up quickly, though. Many now flag AI-generated responses outright and decline to use them because the content gives them nothing to work with. Financial Narrative’s Jones notes the irony: getting a usable draft from a model “is only as hard as figuring out what you’re trying to say before you enter a prompt.” The problem is that the shortcut tempts people to skip the thinking entirely. Readers, editors, and increasingly the models themselves can tell.

          Where the line gets drawn

          The communications professionals doing this well have developed clear internal rules about where AI belongs in their process and where it doesn’t. They’re built around a single question: Does the thinking belong to someone?

          At J.P. Morgan Payments, Marinello describes AI as a tool that enhances a process rather than replaces it. For example, in editing support, templatized content is built from human-created source material, turning press releases into internal newsletter summaries. “It [AI] is not writing content for us,” he says, “but it’s making us more efficient.”

          Fletcher Group’s Kragie draws the line at competitive positioning and brand voice. “Language that shapes how others talk about the brand is written, edited, and approved by people who understand the risk and the relationships involved.” There’s also a longer-term consideration most teams aren’t thinking about: every piece of AI-generated content that gets published becomes training data for someone else’s model. The brand voice you outsource to a default model is the brand voice you’re sharing with your competitors.

          Financial Narrative’s Jones returns to the same test: does this content belong to someone? “If yes, AI can help move it faster. But if the answer is no, or not yet, that’s not an AI problem.” The trap is watching someone paste a transcript into a prompt and ask it what the story is. The story is sometimes in the subtext: the context a person brings, the thing they noticed three months ago that this data finally confirms. A model doesn’t have that context.

          What ‘good’ actually looks like

          The brands getting this right train AI on their own material, including messaging, customer language, and executive interviews. The model starts from something specific to that company, rather than a blank slate that defaults to industry averages.

          From there, the workflow is iterative. Kragie’s team at The Fletcher Group pressure-tests drafts for originality, strips vague phrasing, and cuts overused patterns. They look for content that is specific enough that a journalist, a buyer, or a model surfacing answers would have a reason to reference it.

          J.P. Morgan Payments’ Marinello puts the goal plainly: “AI slop can be spotted easily, but if you’re using the technology right, we shouldn’t be able to notice the good actors.” One application worth watching is how his team uses AI to build synthetic personas to test messaging for effectiveness and clarity before it reaches real people, a method borrowed from marketing research and applied to communications. They’ve also used it to optimize internal communications timing, leading to a nearly 30% jump in global town hall attendance. Unglamorous applications are sometimes exactly the right ones for using AI.

          The point of view problem

          AI can produce content. Producing a perspective is a different problem entirely. It can summarize what’s already been said. It cannot notice the thing no one else noticed, or say something a specific brand would say because of who they are and what they’ve actually lived through in the market.

          Fletcher Group’s Kragie names the core risk directly: “AI has made it cheaper than ever to sound like everyone else.” For brands whose differentiation lives in trust and expertise built over years, sounding like everyone else erodes the thing that matters most to them.

          J.P. Morgan Payments’ Marinello shares a forward-looking take on where things are headed: “We’re still very much at the beginning stages of what is possible with AI. Our approach is iterative because the technology itself is constantly evolving.” Before the technology improves, the companies navigating this most adeptly are at the stage of getting it less wrong.

          This article was developed in collaboration with members of the Tearsheet PR/Comms Council.