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Reading Room

Vol. 1 · No. 75 Wednesday, November 11, 2026 aikansh.com

This week's theme

Who lets AI in, and who keeps it out

Banks are shutting AI agents out to protect free deposits, while a law firm, a coder and 20,000 veterans show what letting it in actually looks like.

A 15-minute read · 7 stories

In this issue

01 Front Page

Big banks lock AI agents out of accounts over lost cash

Big US banks shut AI agents out of customer accounts, and the real reason looks like $1.6 trillion in free deposits.

Big banks just shut their doors to AI agents (software that can take steps on its own, like checking rates or moving money, without a person clicking each button), and the reason is money, not safety. This matters because if you were counting on an AI agent to handle anything inside a bank account, that is no longer possible at most major US banks as of this month.

JPMorgan went first on October 13, cutting off AI agents from directly touching customer accounts. Citi and Bank of America followed within days. Morgan Stanley, Schwab, and Canada's four biggest banks did the same soon after. Wells Fargo took a different approach: starting November 24, if your AI agent does something in your account, the bank treats it as if you did it yourself, shifting the risk onto you.

The official reason given is security. The real reason looks like $1.6 trillion. That is how much JPMorgan, Bank of America, and Wells Fargo hold in deposits that pay no interest at all. Paying even 3 percent interest on that money would cost those three banks $47 billion a year combined, almost half their combined profit. An AI agent that can shop around for a better rate and move your cash there threatens exactly that pile of free money. Meta's AI assistant, Muse, reached 10 million users in six weeks, showing how fast people adopt an agent once it is useful. Apollo's chief economist has a name for what banks fear: an 'agentic bank run,' meaning AI agents could pull deposits out faster and more easily than people ever did by hand.

One bank shows both sides of this at once. Fifth Third Bank told analysts on October 19 that it also does not let AI agents start transactions in customer accounts. Two days later, the payments company Stripe launched a product called Treasury for agents, which lets a business have ChatGPT or Claude pay its invoices and move its money automatically. The fine print says Stripe itself is not a bank: the actual checking accounts are provided by, of all banks, Fifth Third. Fifth Third had quietly agreed back in 2024 to power Stripe's banking product behind the scenes, and Stripe simply plugged AI agents into that existing pipe two years later.

That one company is the whole story in miniature. The part of the bank that customers see and log into is pulling up the drawbridge. The part that just holds the money and the banking license is already being rented out as plumbing to whoever builds the AI layer on top. Bill Gates is widely credited with saying in 1994 that 'banking is necessary, banks are not.' This year's events suggest he was only half right: banks are not disappearing. What is at risk is the bank's front door, meaning the login screen and the habit of just staying put, which earns banks a large share of their profit.

The newsletter that reported this promises more underneath a paywall: an 11-week timeline of every bank's move, who is legally on the hook when an AI agent makes a costly mistake under US and EU rules, and five dated predictions on what happens next. Those specifics are not public here, but the pattern so far stands on its own: do not assume an AI agent can act freely inside a regular bank's own app right now. The open path, for now, runs through products like Stripe's, built on a bank's license underneath, not through the banks' own front doors.

via Linas's Newsletter →
02 Also on the Front Page

Anthropic reportedly targets $1 trillion IPO valuation

Reuters reports that Anthropic, the company behind the Claude models you use daily, is aiming for a $1 trillion valuation in a future stock market listing (an IPO, when a company's shares start trading publicly). The number is being read as a test of how far Wall Street's appetite for AI companies really goes, which matters since it affects the tools and pricing you depend on.

via Google News →
03 Key News

A law firm's CEO shows how AI actually got adopted

Outside your usual reading: this is a case study, not breaking news, but it is a real example of a cautious, knowledge-heavy business putting AI to work, not just talking about it. Sam Nickless is Partner and Chief Executive Officer of Gilbert + Tobin, one of Australia's leading corporate law firms, working in deals, mergers, disputes, and technology law. Law is exactly the kind of profession that moves slowly and carefully by nature, built on specialist judgment and the careful handling of sensitive client information, so how this firm rolled AI in is a useful comparison point for pushing AI deeper into any serious business.

The firm set three goals for AI: serve clients better, make the firm a good place to work, and stay a market leader. Nickless says the real payoff so far has not been cutting cost or headcount. It has been freeing people up to do the more human side of their work.

The rollout had clear stages. First, when the chatbot ChatGPT became available, the firm let people experiment freely, with one rule: no client data or confidential information was allowed into it. That gave staff a safe space to learn what the tool could do. Next came a secure, paid business version, ChatGPT Enterprise, run in a controlled setup with data kept in Australia, contract-level privacy protections, access limited by a person's role, and admin controls over what people could do with it. From there the firm moved to defined, multi-step AI workflows, meaning the AI carries out several steps of a task on its own, with a person still required to review and approve the result before it counts as done.

Leadership set the tone by using the tool first. Nickless built himself a personalized AI assistant that could give feedback in his own style, and it became more valuable once he shared it with his team, who can now get 'his feedback' through ChatGPT directly.

The clearest number in the piece is small but telling. Nickless used the paid business version of ChatGPT during a confidential annual review process covering the firm's 120 partners, asking it to update files from a spreadsheet, build PDFs, and bundle the finished material. It finished in minutes. Checking the usage dashboard afterward, that whole piece of work had cost about $62 in AI usage, against what would otherwise have needed several people working across multiple days.

Nickless's advice to other leaders is concrete: use the technology yourself on real work first, understand where it breaks down, and let your own use set the example. Pair that with clear rules and role-specific examples, give people a safe way to try ideas, and keep a person responsible for the final result no matter how the work got done. His bigger point is that the prize is not just doing the same work slightly faster. Used well, he argues, AI can raise both the quality and the sheer amount of work an organization produces, freeing up people's time for the complex, judgment-heavy work that actually needs a human.

via OpenAI →

Over 20,000 veterans claim free year of ChatGPT Plus

More than 20,000 veterans and transitioning service members have claimed a free year of ChatGPT Plus (OpenAI's paid chatbot tier) to help prepare for civilian careers. It is a small but real sign that AI is already part of career transitions, worth keeping in mind for any coaching or career work.

via OpenAI →
04 Learnings

Claude Code found a hidden beep by building a tracking tool

A step by step example of pointing Claude Code at a real household problem instead of a coding task.

This is a clean worked example of giving Claude Code (Anthropic's AI coding tool) a real physical problem instead of a normal software task, a pattern worth copying for your own small automation ideas. Simon Willison had something beeping intermittently inside a cluttered storage room in his house. Standing in the room and listening did not work: the beeps were too far apart and the room was too full of unopened boxes to search by hand. So he had Claude Code build him a tool called Beep Locator that uses several of his own devices, phones, iPads, a laptop, scattered around the room to work out where the sound was coming from.

The setup: one device runs a small Python web server and shows the main screen. Every other device, he used two iPads, a phone, a MacBook Pro, and a Surface Ultra, joins the same link to become a listener, asking for microphone access in the browser. Each listener runs a simple beep detection algorithm that uses a Fast Fourier Transform (a standard way to break a sound down into its underlying frequencies) plus a high-pass filter to identify potential beeps.

The system compares how the beep arrives at each device to triangulate its source. The underlying method comes from a 2007 research paper, BeepBeep, which showed you could locate sound using nothing but a speaker, a microphone, and a way for devices to talk to each other, no special hardware needed. Willison's system found the beep's location to within about 40 centimeters. He dug through that one pile of boxes and found an old carbon monoxide detector that needed new batteries.

He started on his phone using Claude Code Cloud (a version of Claude Code that runs on Anthropic's servers rather than your own laptop) running Opus 5.5 (one of Claude's AI models) in an extra thinking mode. Working through the problem with Claude, it researched the beep-locating problem itself, found the BeepBeep paper, and wrote up a plan before any code existed. Only after that plan looked solid did he say "try and build it," adding one extra request: build in a way to push new code to the listening devices remotely, so he could leave the system running in the room and keep improving it from elsewhere. That feature, a method that lets the admin server send and run new code on any connected device over its live connection, let him keep refining the detector afterward without physically touching each device.

The lesson for his own Claude Code habits: write the problem down in plain language first and let the model research and plan before building, then keep a remote path to tweak the running system rather than restarting from scratch.

It gave me the position of the beep to within about 40cm
via Simon Willison's Weblog →

Four ways to build a list of local SMB owner contacts

A saved playbook for finding small business owners' direct contact info, useful for the acquisition search.

This is directly usable for the SMB (small and medium sized business) acquisition search, since finding and reaching real owners is one of the hardest parts of that work. The note lays out four separate ways to build a contact list for small business owners in a given area, because local shops, contractors, and boutiques rarely show up well in standard corporate databases built for bigger companies.

The first and most scalable method is scraping Google Maps listings and then enriching them. Google Maps is described as the most accurate, up to date public list of operating local businesses, and tools like Outscraper or the Instant Data Scraper browser extension can pull out business names, websites, phone numbers, and addresses for a chosen area and category in bulk. From there, you take the business website domains and feed them into a workflow tool like Clay, or a database built specifically for small businesses like Openmart, which crawl the web and social accounts to find the owner's actual name and a verified personal email address.

The second path skips building your own scraper and uses a ready made database instead. The note names three: Origami, built specifically for matching physical local locations to the person who runs them; Apollo.io, used by filtering to specific zip codes or cities and narrowing the "employee count" filter down to 1 to 10 or 11 to 50 people, which isolates small owner run businesses; and Salesgenie or Data Axle, described as strong for building targeted local mailing and email lists verified by industry and location.

The third path is public records, which come straight from legal filings. Every US state runs a searchable Secretary of State business registry where typing in a business name reveals the names of its managing members or registered agent (the person or company legally listed to receive official notices), which for a small business is almost always the actual owner. Local Chamber of Commerce chapters also keep public member directories; they will not usually list a private mobile number, but they offer a direct, warm way to reach local decision makers who already want to network.

The fourth path is checking a business's own online presence. Smaller local businesses, landscapers, salons, bakeries, often treat their Facebook Page as their main website, and the "About" section on that page frequently has a direct cell number or a personal Gmail address sitting in plain view. The Better Business Bureau's listing pages are flagged as useful too: they include a specific "Business Management" section that names the primary owner or principal partner directly.

For the acquisition search, the next concrete step is picking one target geography and one of these four paths, most likely the Google Maps plus Clay or Openmart combination given it's labeled the most scalable, and running a first real list through it rather than researching further.

via His own note →
05 Tools & Craft

A developer moves SSH keys into the Mac's hardware vault

A step by step walkthrough of locking SSH keys inside the Secure Enclave instead of a plain folder.

Outside your usual reading: a developer named Serge Gatezh wrote up how he moved his SSH keys (the files that let a computer log into a server without typing a password) off his Mac's hard drive and into the Secure Enclave, a separate security chip inside the Mac that is walled off from the main processor. This is on your desk because any key sitting in a plain folder like ~/.ssh/ can be copied by any script that touches the disk, and a leaked key is often the real way servers and agent setups get broken into, not some clever hack.

Gatezh laid out four options he considered: the Secure Enclave built into macOS itself, a third party app called Secretive that also uses the Secure Enclave, a YubiKey hardware security key, or storing keys inside a password manager like 1Password or Bitwarden. He already uses 1Password but says he might switch to something else in the future. What he wanted instead was a setup where the private key is physically impossible to pull out, even if the Mac itself were fully compromised. That left him choosing between the YubiKey and the Secure Enclave, and he tried the Secure Enclave first.

Since macOS Sequoia (and the newer Tahoe release), the system supports SSH keys that live entirely inside the Secure Enclave. The private key is created inside it and never leaves. When you connect to a server, the Mac asks the Secure Enclave to sign the login challenge, and if fingerprint login (Touch ID) is turned on, you have to approve each one. The private key never exists as a file on disk or even in memory. What sits in your SSH folder is just a reference pointer that is useless on any other machine.

The setup itself took six steps. First, a command called sc_auth create-ctk-identity creates the key inside the chip, using a type of encryption key called P-256, marked non-exportable, and set to require a fingerprint for every use. Gatezh hit an error here (his fingerprint enrollment had somehow reset) and fixed it by re-adding his fingerprint in System Settings. Second, he ran ssh-keygen pointing at the Secure Enclave driver, which produces two files, id_ecdsa_sk_rk and its matching public key, which he moved into ~/.ssh/. Third, he set an environment variable called SSH_SK_PROVIDER permanently in his shell startup file so SSH would use that same Secure Enclave driver. Along the way he flags that most online guides tell you to run an old command, ssh-add -K, which Apple has retired and which does not work with this kind of key anyway. Fourth, he pulled the public key out with a simple cat command. Fifth, he copied that public key onto his server with ssh-copy-id. Sixth, he had to configure his local SSH settings file, because at that point SSH still was not picking up the new key.

That is where the source post runs out, mid troubleshooting: his own debug log shows SSH still offering only his old RSA key instead of the new hardware backed one, and the text cuts off there. His stated bottom line, though, is clear: he decided not to adopt the plain native Secure Enclave approach day to day, because the command line workflow felt too clunky to live with. The post picked up real attention on Hacker News, 260 points and 93 comments, which tracks: it is a genuinely useful security upgrade for anyone running scripts or AI agents that reach real servers, just not yet a polished one.

via Serge Gatezh →
The Last Word
The bank's newest security threat: a customer whose software compares interest rates.
The Desk Report

How this edition came together — from bookmarks and feeds to the page.

242links gathered
40read by the desk
7made the edition

Where they came from

On the cutting-room floor — 33 links read but not run this week

Quality over volume: most links get a second look and a pass. The ones that made it earned their place.

Reading Room — every Sunday

The week's AI signal in 15 minutes — what happened, why it matters, and what to do with it. Curated by someone who actually builds, not a feed algorithm.