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Curve founder pitches market-based fix for $700K bad debt in contrast to Aave bailout

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Curve founder Michael Egorov has proposed a market-based fix for about $700,000 of bad debt tied to LlamaLend, Curve’s lending platform.

“I propose a free-market based method of recovery with option-like payoff, working as an investment for everyone who wants to participate in the effort,” Egorov wrote in the governance post, adding that Curve DAO is “invited but not required.”

The loss from the bad debt sits in LlamaLend’s CRV-long market, which lets users borrow Curve’s crvUSD stablecoin against CRV, the protocol’s governance token. The trade works as a bet that CRV will hold its value or rise. If CRV falls too fast, the collateral may not be sold quickly enough to repay lenders in full.

That is exactly what happened after the Oct. 10 crash, after President Donald Trump announced tariffs on all Chinese goods via a post on Truth Social.

Rather than ask Curve’s DAO to cover the shortfall, Egorov wants to package the affected lender positions into a tokenized vault and let traders buy and sell them through a dedicated Curve pool.

The goal is to give trapped lenders a way out while letting outside buyers decide what the distressed claims are worth.

LlamaLend’s bad debt

The bad debt resulted from the crash, which saw more $19 billion in leveraged liquidations within hours, the largest single-day deleveraging on record.

Curve’s crvUSD minting markets held up during the sell-off, but LlamaLend did not fully escape the damage. Prices fell fast while gas costs rose, leading to a scenario where some liquidations could not happen in time.

Lenders in the CRV-long market were left with deposits backed by about 70% of their stated value. The market is designed to reduce that risk through an automated market maker built into the lending system LLAMMA. Instead of selling a borrower’s collateral all at once when prices fall, LLAMMA converts the collateral in steps as the market moves.

“The providers of borrowable liquidity in this market were exposed to losses during liquidation protection,” Egorov wrote. As a result, he said, they “cannot withdraw their positions,” which are “currently around 70% backed.”

But during the Oct. 10 crash, the market moved too fast. Arbitrage traders, who help keep the system balanced by buying and selling across price gaps, could not keep up. Some lender positions ended up in a vault token that cannot be redeemed at full value today.

Egorov argued the token still has value because the loss is not open-ended. The distressed positions already hold crvUSD that was converted from CRV, so further CRV declines should not deepen the shortfall.

If CRV rises above roughly $0.96, the conversion starts to reverse and the positions begin taking in CRV collateral again. Full recovery would happen around $1.24.

“If CRV price grows up, positions with bad debt will deliquidate,” Egorov wrote, meaning the system would start converting crvUSD back into CRV collateral. “If, however, CRV goes down, collateral is already converted to crvUSD, so the vault deposits will not be less backed.”

CRV is at the time of writing trading near $0.23, well below both levels.

The proposed pool would use Curve’s Stableswap design, with a 1% swap fee and liquidity centered around 71% solvency rather than full value. That means the pool would not treat the distressed token as if it were worth one dollar on the dollar. It would price the token closer to the amount currently backing it.

For trapped depositors, the pool offers a choice. They can keep waiting for a CRV recovery or sell their vault tokens at a discount and move on.

For buyers, the trade looks like a long-term bet on CRV. They buy a claim that is partly backed today and could become worth more if CRV recovers.

That makes the token have what Egorov called an “interesting option-like property,” on CRV’s recovery, but with some backing already in place.

“ts fair price and price floor go up if CRV price goes up, and does not go down if CRV price goes down,” he wrote,

Liquidity providers in the new pool would earn swap fees and any CRV incentives that Curve’s DAO chooses to allocate. Admin fees would partly accrue in the distressed vault token itself. Egorov has asked the DAO to keep those tokens rather than convert them, which would slowly move some of the bad debt onto Curve’s balance sheet through trading activity.

Solving bad debt in DeFi

The timing gives the proposal added weight. Earlier in the month, an attacker exploited Kelp DAO’s LayerZero bridge and released 116,500 unbacked rsETH worth about $292 million. The attacker then deposited that unbacked rsETH into Aave as collateral and borrowed real WETH against it.

Aave now faces up to $230 million in bad debt. The industry response has been a coordinated bailout through DeFi United, a recovery effort led by Aave service providers that raised about $160 million of the roughly $200 million needed so far, with contributions from Mantle, Aave DAO, EtherFi, Lido and Aave founder Stani Kulechov.

KelpDAO, one of the entities affected by the exploit, has committed 2,000 ETH to DeFi United, joining a group of major Ethereum-linked organizations. It’s currently unclear whether LayerZero is participating in the initiative.

Egorov is presenting Curve’s pool as a different model. Rather than pass the hat across the industry, Curve would build a market for distressed claims and let buyers decide the price.

“If this proves to be a successful pilot study,” Egorov wrote, it could be applied in “similar difficult situations” at Curve or other protocols.

Cross-border B2B stablecoin payments to hit $5 trillion by 2035, says Juniper Research

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International stablecoin payments among businesses will total $5 trillion by 2035, fintech analysts Juniper Research said in a new report.

That figure would be 373 times greater than the estimated total value of $13.4 this year.

“Stablecoins are increasingly embedded in cross-border business-to-business (B2B) transactions, treasury operations, and supply chain settlements, where their programmability and 24/7 settlement finality offers advantages over correspondent banking rails,” the research firm said, adding they are “causing disruption to correspondent banking channels.”

Juniper said the growth is driven by stablecoins increasingly addressing the current inefficiencies within cross-border payments that traditional finance handles.

The firm estimates that 85% of the total stablecoin transaction value in 2035 will come from B2B, with the fiat-pegged cryptocurrencies shifting from a speculative asset to a foundational layer of institutional payment infrastructure.

Stablecoins are increasingly integrated in international payments among businesses, treasury operations, and supply chain settlements, because their speedy 24/7 settlement finality offers advantages over correspondent banking rails, the firm said.

“Stablecoins are not replacing payments infrastructure; they are being adopted where the advantages are most pronounced,” said Juniper Research Analyst Jawad Jahan. “Cross-border B2B is where those advantages are greatest, and where we expect the most sustained volume growth over the forecast period.”

He suggested stablecoin issuers should focus on enterprise integrations and treasury partnerships to capture the majority of this value.

Earlier this month, Chainalysis said stablecoins were on track to become a foundational layer of global finance, with adjusted transaction volumes projected to reach $719 trillion by 2035. The blockchain intelligence firm also said that when crypto becomes the default for the next generation, “the question is no longer if stablecoins compete with traditional rails, but how quickly they replace them.”

Clearwater Analytics on The Real Buy-Side Challenge

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At TSAM London, Lotte Tønsberg from Clearwater Analytics discussed the biggest challenge currently facing buy-side firms which isn’t choosing between best of breed solutions and a full front to back system; it’s defining a robust data strategy. Every firm in the buy-side space is struggling with the same fundamental problem: securing accurate and timely data for the front office to make crucial investment decisions. 

The path to solving this challenge and creating a “single source of truth” is establishing one foundational data layer which means partnering with a provider that can help form a front-to-back strategy centered on a single data set, ensuring every stakeholder across the firm is consuming the exact same information.

The modernization of investment operations is, as Clearwater Analytics notes, the “topic of the century” so when it comes to selecting the right technology, the number one priority is verifying if a provider is genuinely a SaaS platform. Many firms say they are, but they’re actually not, which is why Tønsberg suggests a simple litmus test: ask if you have to manage upgrades. If the answer is yes, you should look elsewhere. While selecting a new front-to-back provider can seem intimidating, it doesn’t have to be a major “big bang” overhaul. Firms can phase out the implementation by developing a strategy alongside their chosen provider.

Ultimately, Clearwater Analytics advises against focusing on short-term, tactical fixes and being conscious of your overarching, long-term goals is vital for success. Although creating this strategy may seem daunting now, putting a plan in place and rolling it out in a phased approach is what will truly help a business. 

Clearwater Analytics’ key takeaway is simple: don’t wait to move onto a modern SaaS platform as delaying this move will cause firms to fall behind the curve and miss out on essential benefits like efficiency, scalability, and profitability.

Michael Saylor’s Strategy adds 3.2K Bitcoin at nearly $78K per BTC

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Michael Saylor’s Strategy bought 3,273 Bitcoin for $255 million between April 20 and 26, bringing total holdings to 818,334 BTC.

Ethereum’s EEZ could pull other blockchains into its orbit

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The Ethereum Economic Zone aims to unify fragmented rollups, but its broader goal is to extend interoperability to other blockchains, says Ernst.

Brightbeam on Cutting Through the AI Fog

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Kieran Ivers, Head of Insurance at Brightbeam, shared insights into how the Irish-based AI technology firm is helping large companies in regulated industries, such as insurance and pharmaceuticals, navigate the rapidly changing digital landscape. Brightbeam is founded and run by former CEOs who bring a wealth of leadership experience to the table and their core mission is straightforward: to be the most helpful company in the world by simplifying AI for organizations that are often overwhelmed by the noise and “fog” surrounding the technology.

Ivers emphasized that being helpful means moving beyond just selling a product and instead, Brightbeam focuses on understanding a client’s specific pain points and prioritising use cases that can be put into production immediately. He pushed back against the traditional approach of large consultancy firms that often produce lengthy strategy decks but delay actual implementation.

According to Ivers, the value of AI is available today, and the goal should be getting these technologies into production to deliver measurable ROI, rather than waiting on multi-year roadmaps.

One of the most significant shifts Ivers noted is the democratization of software development as while the past decade was defined by large SaaS platforms that forced companies to conform to rigid models, AI allows for the creation of custom solutions that fit existing workflows.

This approach makes change management significantly easier, as it addresses specific company problems rather than just general industry ones. Brightbeam concluded by highlighting that AI isn’t about replacing jobs, but rather covering the high-stakes, high-accuracy work that humans simply can’t reach, ultimately making organizations more efficient and accurate.

Strive Expands Bitcoin Treasury With $61.4 Million Purchase, Holdings Reach 14,557 BTC

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Strive Inc. has expanded its Bitcoin treasury with a fresh purchase of 789 BTC valued at roughly $61.43 million. The Nasdaq-listed firm disclosed the acquisition in a recent filing, reporting an average purchase price of about $77,890 per bitcoin. 

The transaction lifts Strive’s total holdings to 14,557 BTC as of April 24, 2026, with the stack valued at roughly $1.1 billion based on current market prices.

The latest buy marks a continuation of Strive’s treasury strategy, which centers Bitcoin as a core balance sheet asset rather than a peripheral allocation. The company has framed Bitcoin as a benchmark for capital deployment, positioning it as a hurdle rate for investment decisions and long-term value preservation.

The company’s accumulation comes amid an accelerating trend of corporate Bitcoin adoption. Public companies now hold more than 1.15 million BTC combined, worth an estimated $85 billion, while Bitcoin exchange-traded funds collectively control about 1.28 million BTC, Strive said.

Also today, Strategy bought 3,273 BTC for $255 million, pushing its holdings to 818,334 BTC worth about $63.7 billion while lifting its Bitcoin yield to 9.6% and reinforcing its position as the largest corporate holder.

Strive is stacking BTC

Strive’s balance sheet reflects this shift. Alongside its Bitcoin holdings, Strive reported $90.5 million in cash and cash equivalents and additional exposure to Bitcoin-linked financial instruments, including preferred equity tied to Strategy Inc. This structure indicates an effort to combine direct Bitcoin ownership with yield-generating instruments tied to the broader Bitcoin capital stack.

Strive’s recent activity builds on earlier purchases throughout 2026. In March, the company added 179 BTC, bringing its holdings at the time to over 13,000 BTC, while also expanding its exposure to structured credit products designed to support income generation tied to Bitcoin markets.a

Beyond balance sheet expansion, the company is also investing in education tied to corporate Bitcoin adoption. Its subsidiary, True North, plans to host a “Bitcoin for Business” summit in Oregon aimed at CFOs, founders, and treasury managers seeking to integrate Bitcoin into financial operations. 

The initiative reflects a broader push to normalize Bitcoin within corporate finance frameworks.

In March, B. Riley Financial initiated coverage on Strategy Inc. and Strive, Inc., arguing both stocks were undervalued relative to their Bitcoin treasury holdings.

The firm pointed to compressed valuations, with Strategy trading near 1.2x NAV and ASST around 0.9x modified NAV, framing the discounts as an opportunity amid a broader pullback in Bitcoin.

Unstaking Move By Ethereum Foundation Draws Market Focus, A Sell-Off On The Horizon?

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In the ongoing cycle, the Ethereum staking ecosystem is experiencing one of its most significant activities yet, setting new records in the number of ETH staked across the cryptocurrency sector. After a period of increased staking, the Ethereum Foundation is showing reduced interest in ETH staking with the firm’s most recent unstaking move on Sunday.

Ethereum Foundation Unstakes Some ETH

Amid the excitement of Ethereum’s current price uptrend, a notable change in treasury activity is drawing attention to the Ethereum Foundation. The Foundation is once again in the spotlight as the firm unstakes a portion of its ETH holdings.

According to a report from Crypto Rover on the social media platform X, the Foundation unstaked ETH worth over $48.9 million. This action indicates a strategic change in the foundation’s asset management, possibly to support operational requirements, rebalance exposure, or react to changing market conditions. 

When big firms unstake a portion of their ETH holdings, especially during upside price action, it often points to incoming selling activity. Crypto Rover stated that this move implies that the unstaked ETH can now potentially be sold. The expert’s narrative is also backed by the fact that the Foundation recently sold over 10,000 ETH to Bitmine Immersion Technologies a few days ago.

Ethereum
Source: Chart from Crypto Rover on X

Even though the unstaking only makes up a small portion of its entire assets, the foundation’s influence within the ecosystem makes such activity one to be monitored very closely. A continued unstaking by large firms could play a role in shaping ETH’s trajectory in the long term.

Ethereum Foundation may be unstaking in the face of bullish price performance, but Bitmine Immersion has continued to increase its staked ETH holdings. During the weekend, the leading treasury company run by Tom Lee sacked another 112,040 ETH valued at approximately $259.6 million.

Following the move, Bitmine has now staked over 3,701,589 ETH, worth a staggering $8.58 billion at current prices. Crypto Patel stated that this figure represents about 74.38% of the total ETH holdings, which is currently generating a notable yield. Despite being one of the largest ETH treasury firms, Bitmine is still demonstrating robust interest and demand for the altcoin, reflecting its conviction toward ETH’s long-term prospects.

Fees Are Surging On Ethereum Again

After a period of heightened activity, fees are surging once again on the Ethereum network. This development signals rising demand for block space as users vie for faster transaction processing. However, Stacy Muur, the founder of Greendots and a market researcher, revealed that the wrong factors are driving the surging fees.

According to the researcher, this rise appears to be more like crisis-driven activity rather than fresh capital moving on-chain. Since the Kelp rsETH exploit last week, participants’ sentiment has shifted as they moved to withdraw, repay, and move funds out of the network.

Despite being the primary hub for Decentralized Finance (DeFi), most of that panic activity was executed on Ethereum. As a result, Muur stated that high fees on the ETH network imply healthy growth.

Ethereum
ETH trading at $2,320 on the 1D chart | Source: ETHUSDT on Tradingview.com

Featured image from Freepik, chart from Tradingview.com

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Signal in the age of infinite noise

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The amount of analysis available to you right now is greater than at any point in human history.

And yet most people have less clarity on what is actually happening than they did five years ago.

What changed is the scale. When analysis was expensive to produce, there was a natural filter. The people producing it had to know something because the cost of being wrong was reputational and financial. Now that cost is basically zero. Anyone can generate a macro take that sounds like it came from a Goldman desk in five minutes. The noise is growing exponentially while real signal stays roughly constant.

The insidious part is that the noise does not look like noise anymore. It looks like signal. Bad analysis used to be obviously bad. Now it is polished, structured, uses the right terminology, cites the right data. The tools most people are using to produce it are optimized to sound right. Whether the output is actually right is a different question entirely.

Telling the two apart is the whole game now. The same systems flooding markets with noise can be used to cut through it. That is what I have spent the past two years proving – publicly, on X, with every call timestamped and nothing deleted, across geopolitics, energy, macro, crypto, and broader markets simultaneously.

The account grew from nothing to over 140,000 followers organically, with no paid promotion and no name attached. Signal Core on Substack, the home of the full forecasting operation, became the #3 best–selling crypto publication on the platform within nine months. In a market drowning in noise, the signal alone was enough.

The moment

The signal-vs-noise problem has arrived at the worst possible time.

The next twelve months will reshape more of the financial, technological, and geopolitical order than the past decade combined. Digital assets are integrating with the traditional financial system at a pace that would have seemed impossible eighteen months ago. Regulatory frameworks stalled for years are being rewritten in real time. AI is transforming how capital gets allocated. Geopolitical orders are realigning. Monetary policy is at an inflection point. The labor market is being restructured in front of us.

These are foundational shifts, arriving simultaneously, and compounding on each other. And this is exactly the moment when the ability to see clearly has collapsed. There has never been more at stake and never less clarity on what is actually going on.

The convergence problem

It is actually worse than a noise problem.

AI is converging everyone toward the same wrong answers simultaneously. When a thousand people use these tools to analyze the same event, they do not get a thousand different perspectives. They get minor variations of the same default output. The tools do not just fail to produce signal – they manufacture false agreement.

Before AI, if five analysts said the same thing, that meant something. Now if five hundred accounts say the same thing, it might just mean they all used the same tool.

What this looks like in practice

In January of this year, the prevailing view was that a direct U.S.–Iran confrontation was unlikely. The diplomatic channels were still open. The market was not pricing meaningful conflict risk. Oil was trading like nothing was coming.

The structural picture told a different story.

More than a month before the strikes began, the indicators were already pointing to a confrontation that was more likely than not. We flagged this publicly on X on January 13 while the crowd was still dismissing the risk. When the strikes hit, and oil nearly doubled, the move caught most of the market off guard. The signal was there. The crowd just was not looking at it.

The inputs we were watching were not exotic. Public statements, internal economic pressure inside Iran, and the absence of certain de–escalation patterns. Anyone with access to the open internet could see the same things. The edge was in synthesis – reading those inputs as a single converging system rather than as separate news streams. That synthesis is the hard part. The inputs are just the inputs. The bottleneck has never been technology. It has been how the technology gets used.

This is the pattern. The information was available. The tools to process it were available. What was missing was the ability to read the signal before the crowd formed around the wrong interpretation.

The scarce resource

Most people use AI to generate. Very few use it to see.

Signal is when you can look at a situation that has the entire market confused and see the structure underneath. It is when you can hold a position that every feed is telling you to abandon, and hold it anyway, because you can see something they cannot.

The challenge for most people is not generating signal themselves. It is recognizing who actually has it. Most analysis is hedged to the point of meaninglessness – strategies for avoiding accountability dressed up as analysis.

The old filter for getting past this was credentials. It no longer predicts who is seeing clearly. Plenty of the biggest calls in recent years have been missed by traditional institutions and caught by people working outside them. What matters now is whether someone is actually seeing what is happening – recognizing patterns the crowd is missing, naming what is real before it is obvious, and being right about it often enough that it holds up over time. Once you can see clearly, you start operating on a different timeline than the rest of the market.

What comes next

We are entering an era where signal is the most valuable and least understood asset in the market. The investors, builders, and allocators who figure this out first will have a structural advantage that compounds over years. The ones who keep consuming the flood without questioning it will keep agreeing with the crowd. And the crowd will keep being wrong at the moments that matter most.

Finding rooms where real signal still shows up is getting harder. Most of the venues that claim to aggregate market intelligence are just amplifying whatever the models already spit out.

Consensus 2026 in Miami is one of the few that still functions as a filter rather than an amplifier. The people who show up have skin in the game. Their disagreements are real. Their agreements were not manufactured by the same five models everyone else is using. That kind of room is getting harder to find anywhere else. Which is why I will be there – hosting a small invite–only session about what signal extraction at scale actually looks like.

The edge will not belong to whoever has the most information, the fastest tools, or the loudest platform.

It will belong to whoever can see clearly when everyone else is drowning in noise.

That is the scarcest resource in markets right now.

And it is only getting scarcer.

Accounting Moves to the Age of the Agent with Xero & Anthropic partnership.

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Xero and Anthropic have entered a multi-year partnership to integrate the Claude large language model (LLM) into the global small business platform, aiming to transform accounting from a manual process into an automated, agentic workflow.

In an exclusive intevriew with the Fintech Times, we speak to Diya Jolly, Cheif Product & Technology Officer at Xero.

This collab is a reflection of the industry shift to embedding financial tools directly into a third-party AI interface, allowing users to manage payroll, payments, and tax obligations through natural language.

The move follows a period where Xero utilised multiple models, including those from OpenAI and Gemini, to achieve a 97 per cent accuracy rate in bank reconciliations. By partnering with leading AI developers like Anthropic, Xero intends to tackle the remaining three per cent of complex financial tasks, such as split invoice payments and unstructured data sources that traditional ledgers struggle to process.

Moving Beyond Structured Data
Diya Jolly, Chief Product and Technology Officer, Xero

Historically, accounting software has relied on structured data to maintain accurate ledgers. Diya Jolly, Chief Product and Technology Officer at Xero, explained that the integration with Anthropic allows the platform to reason across unstructured information. This capability enables small businesses to upload diverse data sources, such as spreadsheets or information from legacy payroll systems, and map them directly to their financial records.

“Accounting has sat within a web app forever,” Jolly noted during the discussion. “With natural language, you can actually make accounting available wherever the user is and meet the user”. This shift towards “headless apps” means that financial data is no longer siloed within a single interface, but can be interrogated via Claude.ai or other communication channels.

The Rise of Agentic Workflows

The partnership introduces a reasoning layer to Xero’s financial infrastructure through its AI superagent, JAX (Just Ask Xero). Unlike standard automation, which follows pre-defined rules, agentic AI can orchestrate complex tasks from start to finish. For a small business owner, this might involve asking Claude to identify a supplier, check real-time cash flow to confirm affordability, and then generate the invoice or payment without leaving the AI chat interface.

This evolution prompts questions regarding the future of the bookkeeping profession. While some fear that AI will replace human roles, Jolly argued that the technology is designed to eliminate “grunt work” rather than human oversight.

“Bookkeepers will become more advice givers than the doer of grunt work,” Jolly said. She added that while AI can automate repeated actions, it lacks the subjective understanding of a business’s seasonality or specific context—such as whether a lunch expense should be

classified as sales or entertainment. As the industry shifts, the role of the bookkeeper is expected to move from a volume-based profession to one focused on high-value judgment.

Deterministic Records vs. Probabilistic AI

A fundamental challenge in applying LLMs to finance is the conflict between the probabilistic nature of AI and the deterministic requirements of accounting. Xero functions as a system of record where $1 + 1$ must always equal $2$. In contrast, LLMs predict the next likely token in a sequence, which can lead to variations in output.

To mitigate this, Xero acts as the deterministic anchor. The platform serves as a financial operating system that pipes in bank data and manages compliance rules, while Claude provides the analytical layer on top. Jolly emphasised that for critical tasks like tax submissions, a human expert remains essential to sign off and take responsibility for the filings.

“I don’t think you’re ever going to not review something that has to be deterministic and where the repercussions for it being wrong are high,” Jolly commented, noting that users should treat AI agents like junior employees that require supervision.

Security and Data Sovereignty

A primary concern for financial institutions (FIs) and small businesses when adopting AI is data privacy. The partnership is built on a framework where financial data remains within the Xero ecosystem. When a user queries their financials through Claude, the LLM calls a Xero Model Context Protocol (MCP) server.

This setup ensures that proprietary business data is never used to train Anthropic’s models. Instead, Xero serves an applet within Claude, maintaining a private space for the data exchange. This “trusted intelligence” model is designed to provide the benefits of advanced reasoning without compromising the security of the underlying financial records.

Scaling for the Future

As small businesses grow from lean operations to larger organisations, their need for integration increases. Xero intends to remain an infrastructure layer that connects to various point-of-sale (POS) systems, CRM software, and payroll providers. While Claude provides the reasoning capabilities, Xero provides the API surface and connections to thousands of ecosystem apps.

Looking ahead, Xero’s roadmap includes expanding its presence across more interfaces, including potential integrations with popular accounting tools such as spreadsheets and other office applications. The goal is to move toward a “default automated” state for books closing and tax preparation, utilising the specific strengths of different AI models for different tasks.

The collaboration between Xero and Anthropic represents a significant step in the fintech sector’s adoption of agentic AI. By combining the vast datasets of a global accounting platform with the reasoning power of a leading LLM, the partnership seeks to provide small businesses with the level of financial intelligence previously reserved for companies with dedicated CFOs or analysts.