August 2026 - Story Needle

Readers online deserve to know who or what is responsible for the content they see. AI transparency is both necessary and possible.

AI is shaping more online content, from tweaking human-authored content to providing entirely machine-authored outputs. Readers may notice that text sounds AI-written, but in most cases they can’t detect how much AI was used.

The perils of AI fatalism

The position of the AI industry and countless AI users is “get over it.” AI is used everywhere now; society needs to accept it and stop worrying about AI’s inevitable role. Such AI fatalism ignores genuine concerns about AI accuracy and fidelity, problems arising from unclear or absent accountability with generated outputs.

AI fatalism also dismisses the possibility of solutions to AI attribution problems. AI platforms have largely been uninterested in developing solutions. But their lack of interest does not mean solutions aren’t possible.

Defining human-authorship in the AI age

As bots write more content, readers aren’t surprised to learn it was machine-generated. On the contrary: they will often presume that it is. Online readers face fake reviews written by bots, spammy AI marketing, and vague bot-delivered customer service assurances. Trust in information is eroding quickly.

Simply flagging content as AI-generated may not provide much useful information if nearly all content is designated so. Readers want to know how involved humans were in drafting the content. Did they tweak a bot-developed draft, or did they develop the draft themselves?

An authorship question surfaced in a recent editorial in The Wall Street Journal. The editorial, written by the billionaire investor Stanley Druckenmiller, criticized his former mentee, the current US Treasury Secretary. Readers noticed that the editorial sounded like it was drafted by AI. Druckenmiller admitted getting AI help (“I’m kind of proud of using it”), but insisted that the article sounded like him, since he’d been saying the same things for the past 15 years. Of course, it’s possible he asked a bot to draft something new based on arguments he’d made in the past. That’s what LLMs do: generate new text derived from prior text.

AI doppelgangers are becoming more common, further challenging content originality. Some Harvard Business School professors have cloned themselves digitally to deliver an online course. They may look like the original, but what they say won’t be what the real people would have said. It will be an AI-generated interpretation.

Relying on the “voice” of content is not sufficient to determine whether it is original material or was developed with active human oversight.

Readers want to know how much of the content the individual who purportedly created it actually wrote. People want a reliable measure of human authorship. Human authors are accountable: either they are lying to you, or not. Machine-generated content is ownerless — even the putative authors may disavow it if a mistake is spotted after it’s public.

School teachers deter cheating by making students show their work

School teachers face widespread AI cheating by students. Polished term papers are suddenly everywhere. Yet, unlike many consumers, teachers don’t roll over and accept that they can’t control AI.

When a student submits an essay in Google Docs, teachers look at the version history, which provides a complete listing of various iterations of a doc and the differences between them. If no changes are visible, the student likely pasted an AI chatbot output into an empty Google Doc.

Teachers require students to “show their work.” The same principle should apply to online content available to users.

AI content needs a blockchain ledger

AI vendors are starting to watermark outputs generated by their tools. Anthropic, Google, Meta, Adobe, and others are inserting watermarks into outputs. While a start, the watermarking doesn’t provide a complete version history of the role of AI in the development of items.

The Content Credentials embraced by Adobe provide a “manifest” for an item, describing the tools used to create it. Each item has a unique ID.

What’s needed for AI outputs is a blockchain ledger that shows what happened to the item at different stages of its development: who made changes and what tools were used.

Blockchain ledgers are already used to trace the provenance and authenticity of many goods. Blockchain is decentralized, so that parties using different tools or platforms can all follow a common versioning system.

There’s no reason it couldn’t be used to version digital content that’s co-developed with AI. Doing so would help establish the true authorship of items and allow publishers to pinpoint where in the publishing process human- or AI-originating errors were introduced.

Authorship matters: supply chain transparency is crucial

AI-generated content is a commodity. Readers value it less than human-authored content. Human authors are surrounded by AI tools. They deserve credit for their work. And readers want to be able to trust that work.

Those who argue that AI is now the default and that human authorship no longer matters are misreading the situation. Public skepticism of AI has exploded in the past year. Vendors need more robust solutions that provide transparency into AI’s role.

— Michael Andrews

AI is spurring a seismic shift in the production and delivery of digital content. Websites are losing their importance, and as a result, content management systems are losing relevance too. This change is so radical and profound that it is going unnoticed.

Here’s what has changed: machines are now the most important audience for text. Humans are delegating to bots the work of finding and evaluating details contained in text. And bots need different things from the text than people do.

Humans and bots have different motivations

Most humans are more concerned with expressing what they want than with how an agent should perform a procedure to get them what they want. Humans avoid details if possible, preferring tacit knowledge and heuristic judgment. No one wants to read long terms and conditions statements.

Machines, by contrast, depend on details to avoid messing up. They need lots of instructions and often must synthesize details scattered in many places.

People primarily want to read content that discusses what they want and get — that addresses their intent. They are less interested in the details of how machines interpret those intentions. They will only want to look at the verbose instructions and contracts that machines rely upon if and when there’s a problem.

Humans need articles; bots need files

It’s time to let go of the widespread belief that people and machines need the same things from content. This belief depends on the erroneous idea that bots are just like people and evaluate content the same way. It’s become a popular dogma because it serves as a comforting security blanket as organizations work through the disruptive AI transition.

But it’s absurd to believe that human readability and machine readability are identical. Most web pages are too ambiguous for bot agents to rely on. Organizations need to accept that they’ll have to develop different artifacts for human and machine audiences.

People want human-first articles and messages that are short, easy to read, and may even convey emotion and personality to reflect user intent. Even if bots can fake this style, it doesn’t mean bot agents themselves find such prose useful to act on.

People can be open-ended with their intents; bots perform more reliably when they have concrete direction. People choose AI tools in order to read less.

Agents need machine-first text files: elaborate prose that is full of detail and uses unambiguous terminology, even if the words are technical or legalistic. Bots don’t mind reading more, if it delivers what’s needed. But bots aren’t good at mind reading.

Readers have outsourced the reading to bots

In situations involving information or transactional messages, humans are no longer interested in reading web pages. They’d rather a bot tell them what they need to know; they can follow up if they need to know more. All that text designed to entice readers to notice, click, and take action is being ignored now. Page views and dwell times are meaningless. Web pages no longer have a human audience.

These web pages are increasingly not having a bot audience either. The web page doesn’t tell the bot all it needs to know to make the best decision on behalf of the user. Bots aren’t persuadable, but they need detailed instructions on where to find resources and how to prioritize them.

File management is the new CMS

Many vendors promote the idea that their CMS is now some kind of operating system for bots, as though those human-focused articles are what bots need. In reality, bots need a different set of resources: an array of files, each with a distinct role. The CMS is no longer the gatekeeper.

Example of a bot file management system (Cloudflare OS)

The CMS will continue to exist to publish web pages that humans want to read. Not all content will be bot-delivered. People will still want to read human-authored content and will visit web pages. But that will represent an increasingly smaller set of the resources consumed on the internet.

Writing for bots as the primary audience

Human writers will be divided into those who write for bots and those who tell bots how to write for humans.

Most human writers will focus on writing instructions for bots. These instructions will be detailed and often collectively written, since bots must rely on multiple files. Apart from the writers developing these instructions, no humans will ever read them. They are bot-centric text files.

A smaller group of human writers will write instructions for bots on how to translate responses into human-agreeable prose, and how to interpret human-submitted prompts (how to interpret teenage slang, for example).

Legacy practices of writing for the website first will change. Humans are no longer the main audience in the AI era. For writers, it will require a shift in expectations, accepting that the text they write in a file may never be read by another person. New metrics will emerge to provide feedback on the effectiveness of bot-focused text files.

— Michael Andrews