Principal writer at MDM | Content and editorial advisory for B2B tech firms | Fintech, cleantech, cybsersecurity, and SaaS | bylines @Benzingascribwolf.medium.com London and ZurichJoined December 2020
AI can be a force-multiplier for writers. But if you just publish raw outputs, your audience will feel it ... even if they can’t explain why.
You need to know what to cut, what to reshape, and where human judgment still matters.
Full breakdown at The Startup: medium.com/swlh/how-every…
The five AI writing tells:
1⃣Random quotation marks
2⃣Endless bullet lists
3⃣Contrast pivots ('It’s not X, it’s Y')
4⃣Relentless rule-of-three structuring
5⃣Fluent but context-thin conclusions
As writing devices the first 4 are mostly legit, but AI overuses them.
LLMs are all pattern, zero judgment 👇
AI writing detectors are terrible. False positives. False negatives. Most are pulling a bait & switch for 'humanization' services.
But spotting AI-written copy? That’s getting easier.
After 2+ years working with LLMs, I’ve noticed five “tells” that show up again and again.
Once you see them, you can’t unsee them. 👇
@Kpaxs I have this conviction and it’s turned out to be true almost every time. Try, try again, fail, rethink, reset. Try. Luck or karma eventually punches through.
And your brain knows it. You sense when writing is fake or performative. The result: you discount it or ignore it.
Use AI. It's amazing. It can help get you started.
But if you want people to read what you write, take the time to make whatever you publish your own.
The problem with AI content isn't the quality of the prose — bland, repetitive writing has always been there by the bucketload. The problem is AI that writes plausibly.
When writing isn't backed by real feelings and experiences, what you get are just strings of words. Plausible, maybe – but empty. 👇
Agentic capability is improving fast. We believe Proof of Human is becoming critical for the internet and many of the platforms we use (like X).
This paper explains why FaceID, face biometrics & government IDs won’t solve the problem, and what properties are most important.
"If the prediction market algo starts pricing a product launch at a 30% chance of shipping on time, it’s effectively contradicting the official plan. That creates a leadership conundrum: do we ignore the signal and undermine the tool, or acknowledge it and admit the organization’s forecast may be wrong?"
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