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evidence from Covid-era border closures – Bank Underground


John Lewis

Covid travel restrictions limited movement of people but also made cross-border goods trade more difficult.  Did this contribute to the fall in global goods trade during the pandemic, and if so by how much? In a recent paper using a structural gravity model on global trade flows with domestic trade, I show that a full closure reduced trade for a typical country pair by around 19%, implying a peak hit to global trade of about 23% in 2020 Q2. Hits were larger for nearby partners, and were concentrated in road and air freight, with seaborne trade unaffected. These differences explain why some countries could close borders with smaller trade hits than others. Trade rebounded as restrictions eased, suggesting no lasting scarring.

In the first quarter of 2020, governments tightened borders at unprecedented speed, introducing testing, quarantine and, in some cases, full closures. Global goods trade also fell sharply. Was that fall purely down to contracting global activity, or did border frictions rise making it more costly to trade internationally? In a recent paper, I answer this using a gravity framework that includes domestic trade flows and time-varying exporter and importer controls, allowing me to isolate the role of the ‘extra’ cost of selling abroad versus at home: the so-called ‘border friction’ which controls for the effect of reduced supply capacity in the exporting country and reduced demand in the importing country.  I find that the rise in border frictions was substantial, and implies a significant hit to global trade, over and above that which the generalised contraction in economic activity would have implied. To my knowledge this is the first attempt to explore this issue on cross-country data over the full pandemic period (and beyond) using a multi-country gravity model.

Why might travel restrictions affect goods trade?

Travel restrictions target people, but goods trade depends on people crossing borders too. Testing, quarantine and entry bans add paperwork, cause delays and increase uncertainty, all of which might raise the cost of trading across borders relative to selling domestically and thus reduce cross-border trade. Effects also differ by transport mode: road freight is exposed to queues and checks at crossings  air freight lost capacity when passenger flights were cancelled (reducing ‘belly cargo’); and shipping faced stricter port and crew protocols, though containerised cargo could often keep moving with limited contact.

To quantify the trade impact, I adopt a key innovation from the recent gravity literature – including domestic trade: ie goods produced at home which are consumed domestically, (proxied by GDP minus exports) alongside international trade –  to uncover (changes in) frictions to moving goods across borders (see, Yotov (2012); Yotov et al (2016)). By comparing how a country’s cross-border trade moved relative to its domestic trade, I can isolate changes in the extra costs of trading across borders.

Econometrically, the model is estimated with the standard  PPML estimator and a rich set of fixed effects. Exporter-by-quarter and importer-by-quarter fixed effects absorb country-specific shocks to supply and demand (including domestic lockdown effects). Country-pair fixed effects capture time-invariant bilateral factors (distance, common language and so on). Finally, seasonal ‘border-by-quarter-of-year’ dummies remove regular seasonality in cross-border relative to domestic trade. The resulting border coefficients can be read as changes in border frictions relative to 2019.

How did border frictions evolve during the pandemic?

To estimate how border frictions moved through the pandemic, I allow the ‘border effect’, the gap between trading domestically and trading across an international border, to vary quarter by quarter by interacting a cross-border indicator with time dummies. These time-specific border coefficients are plotted below.


Chart 1: Border coefficients over time


Before Covid, estimated border frictions were broadly stable. When the pandemic hit, the model identifies  a sharp, temporary increase in the ‘border cost’ for selling abroad rather than domestically. At its trough in 2020 Q2, the estimated border effect implies around a 27% decline in international trade over and above what would be predicted by the collapse in economic activity. The border friction then fell back as restrictions were relaxed, and the estimates turn temporarily positive in late 2021, implying an ‘overshoot’, as firms caught up on delayed shipments and rebuilt inventories.

How big was the trade impact of travel restrictions?

I then relate this time variation in border frictions to international travel restrictions, as captured by the Oxford Covid-19 Government Response Tracker, which ranges from no travel restrictions, up to full border closure. Including this as an explanatory variable in the gravity equation shows that even after controlling for the broader pandemic shock, tighter travel restrictions are associated with lower international trade.

I then interact travel restrictions with bilateral distance. This tests whether restrictions change trade costs mainly through a distance-invariant ‘border’ component (paperwork, checks and uncertainty at the border) rather than the per‑kilometre cost of moving goods. If so, we would expect larger percentage trade losses for nearby partners, which is exactly what the estimates show. The central estimate implies that moving to a full closure for an entire quarter reduced trade between a typical country pair (ie the trade weighted average distance between partners) by around 19%.

Importantly, the effect varies strongly across distance. The trade hit is larger for geographically closer trading relationships. That pattern fits a simple intuition that border frictions are ‘distance-invariant’, while transport costs rise with kilometres travelled. When two countries are close, distance-related costs are small, so any increase in border friction is a large percentage increase in total trade costs, and trade falls by more. In the estimates, a full closure reduces trade by roughly 27% at the 10th percentile of trading distances (around 450km), but by around 11% at the 90th percentile (around 11,500km).


Chart 2: Effect of border closures by distance


How did transport mode shape the trade hit?

Distance is only part of the story: how goods travel also matters. To explore this, I draw on UNCTAD data on the value of trade carried by sea, air, road, rail and other modes. Because the transport data are annual and do not cover domestic trade, I calculate a pre-pandemic ‘exposure’ measure: for each country pair, how intensively their 2019 trade relied on each mode.

The results are striking. Once I allow the effect of restrictions to vary with transport exposure, the trade impacts are concentrated in road and air (and the small ‘other’ category). In contrast, there is no evidence that seaborne trade was significantly reduced by travel restrictions, and rail effects are also insignificant. This helps reconcile seemingly different national experiences during Covid: for an island economy where most trade arrives by ship, even strict border measures need not translate into a large hit to goods trade, while land-transport based economies heavily reliant on trucking can face much larger disruption.

Putting distance and transport exposure together generates large cross-country differences in the implied trade cost of closing borders. The paper calculates the hit at country level. The blue dots below show the hit to air/road/other flows given by applying the coefficients on travel restrictions, and the interaction between travel restrictions and distance. By definition, the only source of heterogeneity here is differences in average distance travelled. The red dots then show the hit to total flows: this is the hit in blue dots times exposure to air/road/other flows which allows differences in transport model to play a role. This shows that implied hit to total trade from a full closure ranges from low single digits for some sea-reliant economies with distant partners (such as Australia and New Zealand) to close to 30% for the most exposed countries (such as Slovakia or Bosnia. This demonstrates how some countries were able to close their borders at a much lower cost than others.


Chart 3: Estimated hit from border closures by country


Did restrictions leave lasting scars on trade?

Did temporary border disruptions could permanently reshape trade relationships, for example by causing firms to switch suppliers or breaking logistics links? I test this by including ‘backlog’ variables that capture earlier restrictions. The evidence points away from long-run scarring. Instead, once restrictions begin to ease, trade tends to rebound strongly and temporarily ‘overshoot’, consistent with catch-up trade that makes up for earlier shortfalls. Aggregating the estimates across country pairs implies a peak hit to global goods trade of around 23% in 2020 Q2.


Chart 4: Dynamic effects


What are the broader conclusions?

Three broad lessons stand out. First, even when goods are formally exempt, restricting cross-border movement of people can raise the relative cost of selling abroad. This can happen in ways that look like a classic border friction. Second, incidence is uneven. The same policy can have very different trade consequences depending on geography and logistics: restrictions matter more for nearby trading partners, consistent with a distance-invariant ‘border’ cost making up a larger share of total trade costs at short distances, and road- and air-reliant supply chains are particularly exposed. Third, temporary disruption need not mean permanent damage. trade recovered strongly once restrictions eased, with evidence of catch-up rather than scarring.


John Lewis works in the Bank’s Centre for Central Banking Studies.

If you want to get in touch, please email us at bankunderground@bankofengland.co.uk or leave a comment below.

Comments will only appear once approved by a moderator, and are only published where a full name is supplied. Bank Underground is a blog for Bank of England staff to share views that challenge – or support – prevailing policy orthodoxies. The views expressed here are those of the authors, and are not necessarily those of the Bank of England, or its policy committees.

Bluwhale’s Bringing Trust To Agentic Finance Via Blockchain


Bluwhale cofounders Han Jin and Adam Rowell quickly recognized that if consumers are going to trust AI agents to manage their financial security, they’ll need a better system than simply giving those agents blind access to banking and investment accounts. Their creation is an AI-native financial operating system that allows the user to retain control of data, accounts and permissions via blockchain technology and zero-knowledge proofs.

Jin’s AI experience dates back two decades. He said a main motivation behind creating Bluwhale was his fears over centralized AI.

“We fear for the future and what OpenAI and Anthropic can do for your financials.”

Why AI has grown so fast

Like many, Jin didn’t expect AI to progress this fast. Credit the progress of large language models after the advent of ChatGPT and OpenAI. That allowed creators to deliver an acceptable user experience.

However, in the wake of this AI explosion, many questions remain. What’s the best way to train agents? What’s the best way to transfer information to them? How should AI act in the physical world?

The race is on to address these concerns because younger consumers want their AI. Jin said Gen Z doesn’t want to go to banks and financial advisors for financial planning; they expect a more liquid process than sticking money in the bank and (slowly) watching it grow. Bad news for the establishment, as we’re in the early stages of a historical generational wealth transfer.

Why Bluwhale’s betting on the AI/blockchain combination

Bluwhale believes AI will do a better job of financial management. It works around the clock and makes more rational decisions. An organized system could deploy hundreds of agents to act on your behalf.

The Bluwhale system is a blockchain-based orchestration layer where agents use an individual’s financial services information to transact for them. It leverages zero-knowledge proof technology to encrypt information that is only available to user-permissioned agents.

Users can attach their information to a wallet or blockchain-based ID so agents can learn what services are best for them. They initially allocate small sums to control their risk exposure.

“Now, you’re in this self-sovereign environment where your data is protected and safe,” Jin said.

Jin contrasts his approach with the recent OpenAI/Plaid announcement that allows customers to securely connect their accounts to ChatGPT.

“That sounds scary to me,” he said. “Another agent I don’t understand is going to go into my bank account and start moving funds around?”

How Bluwhale is building trust

How does Jin plan on convincing users that Bluwhale’s system is safer? He doesn’t. Agents who want access have to pay users for their attention. Similar to when a credit card company checks your score, but this pays the user while providing personalized services.

“I’m being incentivized to give them visibility,” Jin said. “I’m getting better financial services that are tailored to me, and all of a sudden, this agent is growing my capital.”

Jin envisions Bluwhale being a modern-day Good Housekeeping Seal of Approval for agents appearing in the Bluwhale store. Working with regulators, Bluwhale will assess effectiveness and reliability before providing store access.

That will take time as regulators wrap their heads around the union of AI and financial services.

“We are in this grey zone period where agents work with regulators and rails to protect retail investors’ risk exposure,” Jin said. “They must address security, privacy and risk management.”

Moving beyond Web3 – It’s just a matter of time

Over time, Jin sees Bluwhale moving into Web2. That’s a much more regulated space than Web3. Agentic finance has some hurdles to jump over first.

“If we can manage that user journey in a way that a lot of things that could go bad are controlled through the application, then I believe that long-term, it’s approving the right AI for those services,” Jin said.

How quickly consumers adopt agentic AI also depends on how quickly the industry addresses AI-based scams. Fraudsters are early adopters of most technologies; left unchecked, they could weaponize agentic commerce and give the sector a black eye.

“It’s going to take time,” Jin conceded. “At some point, the regulator is going to crack down on this space. We’re in a period where it’s grey and very unregulated, but the willingness from Gen Z to interact with those services is incredible.”

And why is that?

“With adoption, my biggest bet is always on convenience,” Jin concluded. “Every technology you’ve seen that consumers adopted, no matter how advanced it was, was based on convenience.”



Knicks’ playoff run that ended in a championship and parade is worth at least $380 million to NYC



The Earth formed 4.5 billion years ago, the first Olympic Games were held in 776 B.C., James Naismith invented basketball in 1891, and the five boroughs of New York City were unified in 1898. And yet, in all of that time, the New York Knicks had never once had a ticker-tape parade.

Until today.

When the team won titles in 1970 and 1973, then-Mayor John Lindsay passed on the downtown spectacle entirely. The ’70 team got a reception at Gracie Mansion; the ’73 squad got a luncheon and a City Hall ceremony that drew roughly 2,000 people. This year, Mayor Zohran Mamdani had no such ambivalence. Minutes after Jalen Brunson sealed the title in San Antonio last Saturday, he posted three words on X: “Parade. Thursday. Manhattan.”

By 7:30 a.m. Thursday, the NYPD had announced that all viewing pens were full, more than two hours before the first float moved, with subway service suspended south of Canal Street to manage the crush. At City Hall this afternoon, after the confetti crews were already at work, Mamdani explained what the run had meant.

“The Knicks did not just win for New York City,” he said. “They won like New York City. What is New York if not your back up against the wall, a dream that feels just out of reach, a rent payment you don’t know how you’ll ever make, 99.6% of the world stacked against you. And who are New Yorkers if not people who hear those odds and smile and ask, ‘Why are you giving me a head start?’”

According to the New York City Economic Development Corporation, the Knicks’ post-season run generated an $380 million in economic activity during home games. During the Finals, each home game was worth $90 million. For context: when the Milwaukee Bucks won the championship in 2021, the entire NBA playoff run generated $57.6 million in economic activity for that city.

Part of that gap is because of how the games were structured. NBA Finals tickets are typically priced 200% higher than regular season equivalents, and New York commands a premium on top of that premium—floor seats at the Garden this year ran a minimum of $10,000. The more meaningful driver, though, is out-of-town visitors: roughly 20% of Game 1 purchases in San Antonio came from New York billing zip codes, meaning fans flew to Texas just to keep the money flowing. When visitors travel in from outside the five boroughs, that’s net new money entering the local economy—not a reallocation from one city restaurant to another.

What it costs to host a historic day

The parade itself comes with its own price tag. The total cost for logistics, preparations, and cleanup is expected to surpass $2 million, based on outlays for previous events. Security alone was historic: more than 10,000 officers were deployed—the largest planned deployment in NYPD history, nearly one-third of the department’s entire uniformed force, exceeding even New Year’s Eve in Times Square. The operation included aviation teams, drones, heavy weapons units, explosive detection K-9s, and a plainclothes unit working inside the crowd—all mobilized after 63 people were arrested and 10 officers injured during street celebrations following the Game 5 championship-clinching win.

The net arithmetic still clearly favors the city. A $2 million logistics tab against $380 million in economic activity is a return ratio most investments would envy. The 347,000 applications for just 600 tickets to the City Hall ceremony tells you everything about the demand. The 2026 NBA Finals averaged more than 20 million viewers on ABC/ESPN—the most-watched postseason since 1998—a national broadcast that doubled as a sustained advertisement for New York City at a scale no tourism campaign can replicate.

The Keys to the City that Mamdani presented to the Knicks today were commissioned months in advance, manufactured by Azra Khalfan, designed by Aneesh Bhoopathy, with typography by Tobias Frere-Jones—the designer behind the typefaces of the 9/11 Museum and NYU. The first keys of the Mamdani administration, given to the team that made the biggest city in the country feel, for a few weeks at least, like it had something to prove.

Pew: Half of U.S. adults under 50 get health information from influencers instead of doctors



Tens of millions of Americans now go to TikTok, Instagram and YouTube when they’re worried about their health.

A recent Pew Research survey found that half of U.S. adults under 50 turn to influencers and podcasts on mostly those three platforms when they’re trying to take better care of their health, and they stumble across the content rather than looking for it. 

While many of those health and wellness influencers position themselves as some kind of healthcare professional, Pew’s analysis found that the majority – moms, coaches and entrepreneurs – do not. Instead, they leverage their personal experiences losing weight, living with illnesses or being caregivers to dole out advice. 

Women – who made up two thirds of the health influencers – were more likely to signal expertise by sharing their life experiences, especially with children. They were three times more likely to refer to being a mom than men mentioned being a dad. 

Black, Hispanic and Asian Americans and those without health insurance are  “particularly likely” to get health advice from influencers, demographics that are historically underserved by the medical establishment.  

“We need to do a better job of reaching our patients where they are and building trust and being out in the community and being available digitally,” Dr. Alok Patel, a Stanford Children’s Hospital physician, told ABC News in reaction to the Pew survey.  

More Americans trusting influencers over health professionals risks them also falling for health disinformation online. Fortune previously reported on the fact that seven in 10 people worldwide believe in debunked health myths and nearly half of Gen Z patients disregard doctors’ advice in favor of their friends or social media. 

This trend of opting for an influencer’s word over a doctor’s advice reflects a landscape where medical expertise is less trusted, especially since the COVID-19 pandemic. According to Gallup, trust in doctors’ ethics has dropped 14 percentage points since 2021 and is now at its lowest point since the mid-90s. Gallup polling also shows less than half of U.S. adults also say their overall healthcare is “excellent” or “good”, dropping 10 percentage points since 2020 after “steadily eroding each year.”

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How Warsh’s ‘more nimble’ Fed could deliver what brokers have been waiting for


“His point is that by giving forward guidance, the Fed impacts behavior which then they take into account to make their decision,” Green said. “His thought is maybe we should let the markets actually tell us where things are going rather than necessarily trying to point the market in a particular direction based upon forward guidance.”

Chairs before Yellen, including Alan Greenspan, gave considerably less forward guidance than has become standard practice, according to Green. Warsh’s decision not to submit his own dot plot projection raised its own questions about the tool’s future.

“By doing that in and of itself, he introduces a little uncertainty to the value of the dot plot going forward if the key member of the Fed, the Chairman, is not participating in it,” he said.

On the unanimous vote, Warsh chose long-term credibility over short-term political points by voting for a hold instead of a cut, Green noted.

“In the short run, there was nothing to gain in it,” he said. “In the long run, there may be a lot of benefit in terms of him developing a greater rapport with the people, where he may need to convince them at a later date when the vote might be much closer, and he may have greater influence.”

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Did the Manager Change the Model or Just the Settings


This phrasing carries the work. You are requesting a specific failure, a specific lesson, and a specific structural change. In conversations with allocators and managers across institutional contexts, the responses cluster into three categories.

A strong answer: The manager identifies a certain drawdown episode and describes what structural assumption proved wrong. They distinguish clearly between changes to model settings, such as a lookback window or position-sizing parameter, and changes to the model’s underlying assumptions, such as reformulating how signals interact, restructuring how conflicting information is weighted, or replacing a component whose implicit prior the team could no longer defend. They explain why the same failure mode is less likely to recur, and they connect the lesson to a broader view about what their model assumes the world to be.

A standard answer: The manager describes a difficult period and focuses on the changes made to lookback windows, risk targets, or signal weights. This is the industry baseline. A useful follow-up surfaces whether anything deeper happened: “Was the underlying logic of the model changed, or only its settings?” Honest managers will tell you. Unprepared managers will reach for the language of structural change without the substance, at which point the gap becomes audible.

A concerning answer takes one of three forms. The first is an inability to recall a meaningful failure, which suggests either a short track record or a research process without the discipline of structural post-mortem. The second is attribution of every difficult period to external regime change, with no reflection on the model’s contribution to the loss. The third is a defense of the model’s continued correctness despite the failure. A manager who has never identified a structural assumption they got wrong has either built a model without structural assumptions, which is impossible, or has chosen not to examine them.

QUOTES-Bank of England policymakers set out views on rates outlook in minutes of June meeting




QUOTES-Bank of England policymakers set out views on rates outlook in minutes of June meeting

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Claude Fable 5 for Physicians: 3 Capabilities Genuinely Worth Your Attention



If you’ve been watching AI evolve over the last few years, you’ve probably developed a healthy skepticism about launch announcements. Every new model promises to change everything. Most of them change something, sometimes meaningfully, sometimes barely at all.

Claude has been a little different. Not because of the hype around it, but because physicians and other knowledge workers kept quietly returning to it as the one that actually helped them think, not just produce output.

Its track record in healthcare-adjacent tasks has been building steadily. Earlier Claude versions already scored higher than competing models on prior authorization letter generation across physician-validated scenarios, with no detected clinical hallucinations in controlled testing.

That context matters when looking at what Anthropic released on June 9, 2026: Claude Fable 5.

According to Anthropic’s official announcement, Fable 5 is state-of-the-art on nearly all tested benchmarks of AI capability, with exceptional performance in knowledge work, vision, and scientific research.

The longer and more complex the task, the larger its lead over prior Claude models.

For physicians specifically, three capabilities stand out as meaningfully new. Not incremental improvements, but the kind of step changes that actually shift what’s worth building into your day.


Disclaimer: While these are general suggestions, it’s important to conduct thorough research and due diligence when selecting AI tools. We do not endorse or promote any specific AI tools mentioned here. This article is for educational and informational purposes only. It is not intended to provide legal, financial, or clinical advice. Always comply with HIPAA and institutional policies. For any decisions that impact patient care or finances, consult a qualified professional.

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1. It Can Hold an Entire Case’s Worth of Documents Without Losing the Thread

One of the most persistent frustrations with earlier AI tools was context fragmentation. You’d load in something, get useful output, and then have to start over or manually stitch things together when the model ran out of room. The result was that AI helped you with slices of complex tasks but not the whole thing.

According to Anthropic’s technical documentation, Claude Fable 5 includes a 1 million token context window by default, with up to 128,000 output tokens per request.

A million tokens is roughly 750,000 words. In practical terms, that’s years of clinical notes, lab results, imaging reports, consultation letters, and literature you want to reference, loaded into a single session, all available at once.

This is worth pausing on. The reason context window size matters isn’t a technical stat for its own sake. It changes the nature of what the tool can do. With older models, you were essentially asking AI to help you with puzzle pieces. You still had to hold the full picture in your own head.

With Fable 5, the model can hold the full picture with you, which frees up your cognitive bandwidth for the parts that actually need your judgment.

Anthropic describes Fable 5 as built for long-running, complex work that used to require frequent human check-ins. For a physician preparing for a complex consult, reviewing a longitudinal case before a care conference, or cross-referencing a patient’s records against a clinical guideline, that sustained coherence changes what’s actually useful to attempt.

The model is also designed to stay consistent across long sessions, using file-based notes to maintain and improve its own work as a task progresses. Earlier models would lose track of constraints or contradict themselves midway through a long session.

Fable 5 is built specifically to avoid that, which matters because inconsistency across a complex document is exactly what makes AI output untrustworthy for professional use.

2. It Can Work Through Complex Administrative Tasks End-to-End

Prior authorization is one of those topics that physicians are so tired of that even acknowledging it takes effort.

Physicians submit over 100 million prior authorization requests annually. A 2023 AMA survey found that physicians spend an average of 14 hours per week on prior authorization work, and 94% reported care delays tied to it.

The problem with earlier AI help on this wasn’t willingness. It was stamina. The model would help you draft a letter, then stall on the step-therapy argument, or lose track of the payer-specific criteria, or need to be re-prompted through each section like managing an assistant who has to be walked through every paragraph.

What’s different with Fable 5 is what Anthropic calls sustained autonomous performance: the ability to work continuously on long-running tasks, dramatically outperforming prior models.

In administrative terms, that means Fable 5 can move from a starting prompt through a multi-step task, reviewing the clinical notes, identifying the relevant payer criteria, drafting the letter, building the step-therapy argument, flagging missing documentation, without losing context or requiring you to re-orient it at each stage.

It finishes the job rather than handing it back to you halfway through.

On AI performance specifically: earlier Claude models achieved the highest scores across physician-validated prior authorization scenarios in controlled testing, primarily through stronger anticipation of insurer-specific denial criteria. Fable 5 extends those capabilities further.

The same pattern holds for other administrative tasks that compound across a day. Tasks that each seem small individually but together account for hours physicians never fully recapture.

3. It Handles Dense Scientific Literature at a Level That Actually Saves Time

This one is harder to summarize with a single example, so start with what the data shows.

In third-party testing by analytics company Hex, Fable 5 was the first model to reach 90% on a benchmark of complex, long-running analytical tasks, with Hex noting it shows strong judgment and attention to nuance on the hardest questions.

That score isn’t about trivia recall. The benchmark involves extended analytical reasoning across long documents, exactly the kind of work physicians do when trying to get up to speed on a new drug class, review the evidence behind an emerging guideline, or understand a clinical trial their patient just read about and brought to an appointment.

According to Anthropic’s Fable 5 announcement, the model reached comparable outcomes on frontier research benchmarks in 36 hours to what competing models reached after four days, while using a fraction of the reasoning compute.

The efficiency matters as much as the capability: a faster, more accurate literature synthesis assistant is the difference between doing a thorough review before a complex case and skimming or skipping it because there isn’t time.

Practically, this shows up in tasks like loading in 15 to 20 papers on a treatment approach and asking Fable 5 to surface where the evidence conflicts, what the methodological weaknesses are, and what the clinical implications are for a specific patient profile.

Or asking it to review a set of clinical guidelines from different professional societies and identify where their recommendations diverge.

Earlier Claude models could do versions of this. Fable 5 does it more completely, with less hallucination risk, and across a larger document set. The sustained coherence also means it doesn’t start contradicting itself halfway through a long synthesis.

One scope note worth knowing: according to Anthropic’s safety documentation for Fable 5, biology and chemistry queries are routed to Claude Opus 4.8 as a safety measure.

For clinical literature work and evidence review, this rarely comes up. But if you’re doing research that gets specific on drug mechanisms or chemical pathways, expect some fallback behavior.


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A Quick but Important Update on Access

Before you try to open Fable 5 anytime by June 17 as of writing, there’s something you need to know.

On June 12, 2026, just three days after launch, Anthropic received a directive from the US government to suspend all access to Fable 5 and Mythos 5. The order, citing national security authorities, requires that access be disabled for all customers globally to ensure compliance with export control requirements covering foreign nationals.

Anthropic has complied with the directive while publicly disagreeing with it. Their statement explains that the government’s concern appears to be a narrow, non-universal jailbreak, and that the level of capability demonstrated is widely available from other publicly deployed models.

Anthropic has stated it believes this is a misunderstanding and is working to restore access as quickly as possible. All other Claude models, including Claude Opus 4.8, remain fully available and unaffected.

So if you try Fable 5 right now, you won’t be able to access it. That may change soon. Anthropic has committed to communicating any updates ahead of time, and the situation is actively developing.

This doesn’t change the substance of what Fable 5 represents or what it will be capable of when access is restored. It does add an honest footnote to an otherwise significant release, and it’s a good example of why staying informed in this space matters.

Things move fast, and sometimes in unexpected directions.

AI Development Isn’t Going to Slow Down and Wait for You

It’s worth being honest about what this moment actually represents, separate from the specifics of Fable 5.

The Claude model family has gone from Claude 1 in March 2023 to Fable 5 in June 2026, with Fable 5 representing an entirely new tier above Opus, the family that was itself considered the top of the line less than a year ago. The Opus family alone has seen eight versions since May 2025.

That pace is real, and it’s accelerating. What was genuinely impressive six months ago is already a few capability jumps behind what’s available today. For physicians, the practical implication isn’t that you need to chase every release.

It’s that the gap between “I’ve heard of this” and “I’m actively missing out on something useful” is shrinking faster than most professional tools ever moved.

AI development in this space is compounding, not linear. The physicians who stay curious and informed now, who build one workflow, test one capability, understand what the tools actually do versus what the marketing says, are building a meaningful advantage in how they spend their time.

Fable 5 is a real step forward, not just a version bump. The context window, the sustained task performance, and the analytical depth are all capabilities that change what’s worth attempting with AI assistance.

That doesn’t mean it replaces anything clinical. It means the administrative and knowledge work surrounding medicine has a much more capable assistant than it did three weeks ago.

That’s genuinely worth knowing. But what do you think? Let us know in the comments!


At Passive Income MD, we cover the tools, strategies, and practical AI workflow tips helping physicians build more time and financial freedom. We’ll keep tracking where AI goes from here.


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