Claude’s watermarking is not here to free LinkedIn of AI slop or rat you out when it authors an invitation for you.

Watermarking is here to help Claude gain adoption in enterprise.

Enterprise Risk and AI Governance leaders know what’s happening. They are terrified their internal projects are quietly filling up with AI slop instead of actual human output.

They are hoping a watermark will finally let them see who is doing the work. But this problem runs much deeper than it seems. Using technical features to solve human problems is shortsighted.

Here’s the risk profile that’s at play, as laid out in my Crossfactor Atlas.

1. Workslop. You’ve heard of this. When employees use Claude to write a 6-page memo which will then be summarized by the recipients using the same chatbot.

2. Effort Laundering. A watermark is a hurdle, not a wall. If employees are incentivized to ship volume over quality, they will simply run watermarked text through secondary, open-source tools to strip the cryptographic signal.

3. Institutional amnesia. If you are relying on watermarks to catch slop, you are already losing. By the time you catch it, your team has been substituting AI for experience for months.

4. Surface Credibility. Trust is granted to content based on its presentation (dashboard polish, memo structure, graphics, illustrations etc) before its substance is even assessed.

5. Trust Transference. Once your workforce expects AI output to be watermarked, unmarked material may gain credibility it does not deserve.

6. Unearned Warrant. Content entering your organizations shared record can quickly become disconnected from the initial verification. Information can be re-cited until each hop reads as independent corroboration.

7. Authority Capture. Related but distinct to many of the above. AI output can accrue trust, standing and citational weight within an organization faster than accountability for those outputs is established.

Enterprise leaders are hoping a watermark will act as a lie detector for human effort. But watermarking is an IT solution to a sociotechnical problem. It will fall well short of solving workslop and the other risks listed above.

These are just 7 of the 212 risks I map out in the Crossfactors Atlas.

AI Governance and Risk leads out there: how many of these are you actually tracking today?

TYPECHA analyzes writing sessions across five dimensions: keystroke patterns, sequences of typing bursts and pauses, revision activities, origin (paste detection) and interactions with the editor. It uses this analysis to create a composite score.

After verification, you receive a shareable image like the one in this post. A certificate page is also published which includes certificate details and the verified content. It is your proof that you typed something yourself before you shared it.

So why not try it out and stand out from AI slop?

You could even use it to write out a comment to this very post. Feel free to include the TYPECHA certificate along with your reply.

It’s something bots won’t be able to do.

And if those who generate their LinkedIn content with AI are doing so to reach those remaining human accounts…

And if those remaining human users, seeing LinkedIn’s algorithm reward the AI-generated content begin seeking solutions to do the same…

And if, after adopting those solutions the pressure grows on the remaining human users…

Does that mean that those who post with AI are zombies?

This is part 19 of “101 Ways AI Can Go Wrong” - a series exploring the interaction of AI and human endeavor through the lens of the Crossfactors framework.

View all posts in this series or explore the Crossfactors Framework

The one thing that’s still open at OpenAI is the way they openly rip off the content of beloved artists to train their models - and encourage their users to generate images with them.

Data sourcing is topic #19 in my series of 101 Ways to Mess Things Up with AI.

What is it?

Data sourcing is the collection of training data to build AI models. It may be scraped from the web, obtained from paying customers or anonymously aggregated from patients. Data sourcing precedes other steps such as data ingestion, cleaning, processing, labelling, augmentation, etc.

Why It Matters

The modern age of generative AI is built on massive amounts of data and little clarity has been provided so far to describe its provenance. It is known that a great deal of copyrighted material was used in the training of these models. Even for the case of material that was not copyrighted, the creators shared it for people and not for the creation of genAI models that would later be monetized leveraging their work.

Even prior to genAI, there are many cases of data sourced from customers, employees and even healthcare patients without their knowledge.

Real-World Example

Studio Ghibli is a Japanese animation studio that produced several beautiful films which had an extensive reach across international audiences. In March 2025, an update to ChatGPT enabled users to more easily create images of particular styles, or to style-transfer using an existing image. “Ghiblified” or Ghibli-inspired images started being shared widely online, many of which were existing well known images that had been converted to the well known animation style.

The virality of Ghibli images was quickly set against the backdrop of previous sharp criticism by Hayao Miyazaki, a co-founder of studio Ghibli. A video of this criticism resurfaced. It also struck a chord with many fans of the studio’s work who didn’t like seeing the memefication of the style.

It’s been clear for some time that OpenAI and other AI firms use source material from these works to train their AI models. We saw in a short amount of time both the virality and the ethical quandary caused by repurposing a specific set of works to generate images inspired by it.

Key Dimensions

Legality - there is significant legal and regulatory scrutiny facing the data sourcing practices with several lawsuits currently in play. Issues of copyright, fair use, privacy and deception are all being investigated.

Ethics - even if something is legal, it doesn’t mean it is ethical. Collecting data to serve some other business aims, or only to monetize it may not be ethical Ethics framework in many domains, for example in healthcare where individual data may contribute to the greater good, are struggling to keep up.

Backlash - the public and governments may revolt against data sourcing practices, especially if they object to the end result.

Take-away

Big data and AI means the collection of massive amounts of data. Customers and users will ultimately care about the origin of this data as well as the sourcing practices - but likely not until you’ve already done it.

Cold outreach is nothing new. It predates LinkedIn, email and the internet.

If you’re like me, you’re on LinkedIn to expand your knowledge and network and perhaps even be entertained a bit. Most of us willfully accept a volume of cold-outreach in service of this goal. We easily ignore cold, unpersonalised connection requests and messages while giving more consideration to those showing more effort and relevance.

I believe this balance is delicate and LLMs could upset it.

There had been an inverse relationship between the scalability of the outreach and the personalization it could offer.

On LinkedIn, those who use LLM tools hide the provenance of their outreach - it shows up as coming from their account and signed with their name. The message text has the appearance of being highly relevant to your profile details or a recent post or comment. It often misses the mark - especially if you’re already writing nuanced takes on the use of AI, like me.

I’d rather get a cold outreach that’s truly personalised by a human, or not personalised at all. The middleground is dishonest at best. I don’t want to spend time figuring out if something which has the right keywords at first skim actually makes sense and if it was written by the person who is presenting it as theirs.

Cold outreach is the first step to engage in a business relationship. If you’re using LLMs to do this on your behalf, you are starting on the wrong foot.

Yes / Yeah / Yep

It offered me three easy ways to say yes, from most to least formal. What I really needed was a way to send a quick “No”. There’s two possible responses, possibly a third inconclusive one (“maybe”), but it only offered one option.

This reminded me of the old Windows desktop-driven applications. Who else remembers these? The quintessential scenario was closing a document with unsaved changes. One would be presented with these three options:

Yes / No / Cancel

It always rubbed me the wrong way that three options were given when only two courses of action existed. And before pressing Yes or No, you needed to read the dialog text carefully - would pressing Yes save then close, or would it close without saving? If you were like me, you’d hit cancel, save, then exit once more, hopefully unimpeded by clunky UX - and ironically justifying the existence of the third option.

It took longer than it should, but software makers have moved away from things like the Yes/No/Cancel dialog box. Yes, we have auto-save, particularly for anything cloud-based. But application designers now better account for user intent. Apple, for example, quickly took a step forward by renaming the buttons Save/Don’t Save/Cancel. But generally, current software design principles don’t burden the user with bad UX particularly since a small change in the code can make a big difference to the daily experience.

Back AI-suggested responses - what have we learned? How will software makers ensure the best possible experience for their users while allowing a model to create options that may not be predictable during development? It may have just been a suggested text response in my case but more impactful scenarios already exist and will only become more common.

I believe that new paradigms in UI/UX will need to be developed to account for AI models being integrated into products, much like it was the case with the advent of desktop-GUIs and mobile touchscreens. Until then, please know that two Yes’s from me mean a No.