Issue #7: Human Terms

Spirit Airlines stopped flying this year, and what went to auction was not the planes but the paperwork: about 100 million emails from 80,000 staff accounts, 500 million Teams messages, and 175,658 employee records reaching back to 1986. Google won it on August 14 for $10 million, roughly ten cents an email.

Then the people who wrote it objected. On August 19 the union that represented Spirit’s cabin crew told the bankruptcy court that the deal’s privacy protections were built for passengers, not staff: customer profiles and loyalty records were carved out, disciplinary files and payroll history were not. It does not want to stop the sale, only to have employees scrubbed the way customers already were.

Judge Sean Lane halted the sale and moved the hearing to September 9. The same day a rival bidder offered $12.5 million.

Why this matters to you: every message you send from a work account belongs to your employer, not to you. What is new is that the ordinary texture of your working life now has a resale value, and the buyers build AI. Nobody at Spirit was asked, because nobody had to be. What changed is that a group of laid-off employees noticed, hired lawyers, and stopped a Google deal in five days. The protections in a deal like this get written for whoever has someone in the room.

Sources: Fortune · Bloomberg Law · CNN · Forbes · Inc. · SiliconANGLE

In Issue #4 we noted that OpenAI had discounted its cheaper models and left GPT-5.6 Sol alone, and called that the tell for where it thought its advantage lay. On August 21 it cut Sol too, by a third on the expensive half. Reuters read it as an answer to Anthropic and to Chinese rivals. It runs as a promotion through November 21.

→ So what: every app that has sprouted an AI feature buys at these prices, which is why the summarise button keeps appearing in software you already pay for. Watch that word “promotional”: if it holds in November, prices keep falling.

ChatGPT began showing ads in 31 more countries. On August 24 OpenAI started serving advertisements inside ChatGPT across 31 European markets. Americans have had them since February. They sit in the conversation, labelled, and OpenAI says advertisers never see what you typed. Only Free and Go accounts see them.

→ So what: the thing you ask questions to used to have one customer, which was you. It now has two, and the second one pays. That makes the free version of the most-used AI tool on earth an advertising product, in the place people go for medical and money questions at eleven at night.

ChatGPT has a teen version, and it assigns people to it on an age estimate. Launched for ages 13 to 17, and applied automatically whenever the system estimates a user is under 18. It tightens what the chatbot will discuss around self-harm, eating disorders, violence and sexual content, adds a study mode, and gives parents optional controls. Issue #5 covered the safety tests behind it. This is those tests shipped.

→ So what: if you have a teenager, their ChatGPT changed this week without either of you touching anything. The age is estimated, so it misfiles both ways, and the company writing the protections is the one counting the users.

Sources: Reuters · WinBuzzer · OpenAI on ads · Search Engine Land · Digiday · OpenAI on teens · TechCrunch · Axios

On August 13 OpenAI added a feature called Computer History to its Mac app. Turned on, it keeps a timeline of what you opened, clicked, typed and visited, so you can ask what you were working on before lunch and get a real answer. It takes no screenshots and no audio, the deliberate contrast with Microsoft’s Windows Recall, and it is off until you switch it on. Then there is the last line: on a Business or Enterprise account, an administrator decides whether you may enable it at all.

→ So what: the feature is genuinely useful, and the thing to notice is who holds the switch. On your own machine it is yours. On a work machine it belongs to whoever runs IT, and a record that exists can later be asked for by a lawyer or an auditor. Ask your employer’s policy before you turn it on. Then read the top of this issue again: work makes a record, records are data, data has an owner, and this week some flight attendants proved the owner can at least be argued with.

Sources: The New Stack · PCWorld · TechRepublic · Cybernews

A binder is a molecule shaped to stick to one target and nothing else, like a key cut for a single lock. Almost every antibody drug works this way, and designing one from scratch is slow.

On August 18 Anthropic published what happened when it pointed Claude at the problem. One AI agent researched the targets, chose which public scientific tools to use, generated 1,320 candidate molecules and ranked which to send for manufacture. Then two independent companies, Adaptyv Bio and Twist Bioscience, built them and tested them in a lab. 354 stuck, with at least one working against 14 of the 15 targets: a success rate of 22.6% to 35.1%, against the 10% to 15% Anthropic cites as normal for the field. It failed too. Against maltose-binding protein, none of the 90 designs worked.

→ So what: this is not a drug and nobody has been treated with anything. What got faster is the first step of a long road, and the steps after it are unchanged: cells, then animals, then people, which is where most candidates die. What is genuinely new is the checking. Anthropic published the designs and outside labs made the molecules by hand, so the result does not rest on the company’s own word. In a year of AI claims nobody could reproduce, this one was. Ask who made it and who measured it. None of this is medical advice.

Disclosure: Human Terms is written with help from Claude, which is made by Anthropic. We cover them the same way we cover everyone else, and we say so when it comes up.

Sources: Anthropic · The technical report · Adaptyv Bio · The Next Web · Dataconomy

OpenAI’s Preparedness team had one unglamorous job: take a finished model before anyone outside saw it and ask whether it could help someone build a biological weapon, run a cyberattack on its own, or act in ways its operators could not pull back. Their answers set the rating that governed how it shipped.

Several outlets reported in mid-August that the team was dissolved at the end of July and its work parcelled out to other groups. OpenAI disputes that, and says the work was spread across the company rather than ended. Neither side has moved since. What gives the report weight is the pattern: this would be the third safety group folded in about two years.

→ So what: we have spent two issues reporting what that machinery produced, including a model locked in isolated machines over a possible “critical” hacking rating, and a purpose-built hacking model rated “high” and put on sale. A framework is only as good as the people left to apply it. You do not have to pick a side to see why it matters who those people are.

Disclosure: Human Terms is written with help from Claude, made by Anthropic, which competes with OpenAI.

Sources: Engadget · The Next Web · Calcalist · Kernel

Alphabet, Amazon, Meta and Oracle have sold close to $223 billion of bonds through August 20, more than double what the sector raised in all of 2025. A bond is a loan you can trade. These are investment grade, though the range runs from Microsoft at the top of the scale down to Oracle at the bottom of the safe band with a negative outlook.

Here is the part that reaches you. These four are now about 8% of the main US investment-grade bond index, and index funds buy the index, so if you hold a boring bond fund inside a pension you have been buying data centres without deciding to. Against that, a Wall Street Journal analysis this month put nine tech companies’ off-balance-sheet AI commitments near $3 trillion, versus about $600 billion actually spent in the past year.

→ So what: the buildout is shifting from company profits to borrowed money, and borrowed money is repaid on a schedule whatever the thing you built earns. That is not a prediction of disaster. It is a change in what happens if demand disappoints, because a promise can be renegotiated and a bond cannot. Nothing here is investment advice.

Sources: CNBC · MLQ · Morningstar · Forbes on the commitments

On August 18 Governor Josh Shapiro signed Executive Order 2026-05, turning a voluntary set of standards into a condition of doing business in the state. To build an AI data centre in Pennsylvania a developer must now bring its own power, pay the full cost of new electricity infrastructure rather than passing it to households, meet water and environmental requirements, hire locally, and sign a community benefit agreement.

Two provisions go further than anything we have covered in this thread: local approval must come before the state will even begin reviewing a permit, and nondisclosure agreements are prohibited. Critics note that an executive order binds state agencies rather than writing law, so a future governor can undo it.

→ So what: the NDA ban is the piece to hold onto, because it is the one about you. Developers routinely ask local officials to sign confidentiality agreements, which means your township supervisor can be discussing a project next to your house and be legally unable to tell you about it. If a data centre is being talked about near you, ask at the next public meeting whether anyone at the table has signed something. The answer is informative either way.

Sources: Governor’s office · Philadelphia Inquirer · Pennsylvania Capital-Star · Environmental Defense Fund · FOX43

September 9: the Spirit sale hearing. Judge Lane weighs the flight attendants’ objection and the late $12.5 million rival bid. What he decides becomes the template for every company that goes under from here.
Before midnight on August 31: California’s floor votes. The 24 AI bills that survived the committee cull, four of them on chatbots and children, have to clear a floor vote before the session ends. We owe you the result next issue.
The next six to eight weeks: the Meta trial. Twenty-nine states are arguing to a jury in Oakland that Meta built Instagram to hook teenagers. A recommendation feed is AI, and this is the first jury to decide whether tuning one for attention is a harm a company owes money for.

Sources: SiliconANGLE · Transparency Coalition · CNN · NBC News

Two studies this year read what people actually type into AI at work rather than asking them about it. Microsoft Research went through 105,000 Copilot conversations and Anthropic did the same for Claude. Both found the same thing: the biggest use is not writing, it is thinking. Just under half of workplace AI use is someone working a problem through, and the most common single request is “explain this to me.”

1. The letter you have been putting off. Give it the facts as bullet points, say who is reading and what you want them to feel, and ask for three versions at different levels of firmness. Then rewrite the sentence that matters yourself.

2. The forty-page document you need three things out of. “Summarise this” gets you a shorter document. “What in here would cost me money, what is unusual, what is missing” gets you a list. Then read those parts in the original.

3. Talking a decision through before you make it. Stop asking for a recommendation. Ask it to argue against what you already want, and to list what would have to be true for your plan to fail.

4. The thing everyone assumes you already understand. The acronym that has been in every email for a year, the clause in the tax letter. Explanations are the single most common thing people ask Claude for, because there is finally somewhere to ask without a colleague clocking that you did not know.

5. The recurring task that eats a morning. Cleaning a messy list, the same six replies, numbers into the format the invoice needs. This is the only one where letting the machine finish is reasonable, and only when a mistake would be obvious the moment you look.

One thing not to do: do not treat silence as an all-clear. In the medical benchmark we covered in Issue #4, more than 80% of the severe errors were things the tool left out rather than got wrong. So for health, money or legal, use it to prepare better questions for a human. The best prompt is: what would you want to know that I have not told you?

→ So what: these tools are very good at the first eighty per cent of a job and at helping you think, and mediocre at deciding. The disappointment comes from handing over the last twenty per cent, which is the part with your name on it.

Sources: Microsoft Research · The Copilot study · Anthropic Economic Index · Gallup via U.S. News · Microsoft Work Trend Index

One question for you: The Make It Useful section is built from what people actually type into these tools at work, not from what anyone recommends. So tell me what you want in it. A task you keep doing by hand? A tool you cannot get to behave? Hit reply and tell me what would make your week easier. I read every one.

Human Terms

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