A few weeks ago I posted something I had put real care into. A public verification ledger, dozens of records, a session-start contract that checks an AI maintainer's working state before it touches an archive. The kind of post where the details matter, because the whole point is that details are the only thing that can be checked.
A comment came in fast. What follows is a lightly edited version of it, with names and a few identifying specifics changed, but the shape and the rhythm of the original left intact:
Strong direction, this is exactly the kind of structural thinking the space needs more of.
But what stands out to me is the difference between governing the starting state and governing the live run. A validator catches structural gaps before the first edit. But once execution is live, can we still see what the system is doing, and preserve an accountable record as it unfolds?
That is where our framework fits. Authority before. Visibility during. Proof after. Because session-start control is only enough if governance remains attached all the way through execution.
It was fluent. It was structured. It used my own language back at me, authority, visibility, proof, in a rhythm clean enough that for a second it almost looked like it belonged.
But none of it engaged with what a session-start contract actually does or does not solve. It read like a comment generated from the words "AI governance" and the first sentence of my post, and underneath the analysis, my actual reaction was simpler than that: someone had this written for them, and they never read what I wrote.
A Word For It
Open LinkedIn and you have probably seen the same scene repeat. I went looking afterward for whether anyone else found this as strange as I did, and it turns out a lot more people are uncomfortable with it than I expected.
An entire corner of the internet has been cataloguing it for years. A Reddit community devoted to LinkedIn's stranger habits has grown past 670,000 members, and one of its most upvoted threads is simply titled something close to "every single post is the same post now."[1] A regular complaint running through it is that the platform has become purely performative, all delivery, no actual exchange.[1]
People who shitpost on LinkedIn on purpose, parodying the genre from the inside, have their own word for everyone still posting in earnest around them: NPCs, the background characters in a video game, present in the scene but running on a script.[2]
That word is unkind, and probably unfair to most of the people it gets aimed at. But it points at something real. A feed where everyone is mid-monologue and nobody is actually listening to the post next to theirs starts to look, from the outside, exactly like a room full of characters who are technically there and functionally absent.
I see some version of this every day now, and it is the pattern I am most wary of, the one I have come to actively distrust the moment I spot its shape. Not disagreement. Disagreement is fine, even good. It is the comment that takes one phrase, runs with it, and pivots to a pitch that would have worked under almost any post with those same three or four buzzwords swapped in.
The tell is never the polish. It is that the comment would have read exactly the same if my post had said something completely different.
There is a quieter cousin of the same pattern, and it is almost harder to catch. A different comment, on a different post, opened with "validation infrastructure is the product" is exactly right before pivoting, for the next four sentences, to something the commenter clearly wanted to say anyway, a service offer with nothing to do with verification at all. The opening line proved a glance happened. It did not prove anything past it.
The first version skips the post completely. This one reads exactly one line of it, takes that as license, and is gone.
Then there is the other scene. A short comment that lands exactly on the gap in what I wrote. That person read it. Thought about it. Then spoke.
Why I'm Writing This Down
The difference between these two scenes is not skill. And it is not, I want to be clear about this, whether AI was involved in writing either comment.
No one objects to hiring a translator who says exactly what you meant to say. No one objects to a CEO who has a lawyer draft the contract or a speechwriter shape the delivery. Those are agents, doing what a principal directed them to do, carrying real intention from one place to another. The objection was never to having help. It is to having no one home at all.
That distinction has a name in the research on AI agents, and it turns out to be more exact than the metaphor suggests. Researchers studying agentic AI through what is called principal-agent theory describe a human delegating a task to an AI system the same way an employer delegates to an employee: the principal sets the goal, the agent pursues it, and responsibility for the outcome stays with the principal.[3]
A lawyer or a translator fits that frame cleanly, because a human agent can exercise judgment, notice when something does not fit, and escalate back to the principal when the situation calls for it.[4]
An AI system fits the frame less cleanly, and the gap matters. Researchers have pointed out that an artificial agent cannot be held accountable the way a human one can, because accountability has historically rested on intention, and intention is the one thing the system does not have.[5] It does not decide to skip the reading.
It has no stake in whether the post underneath it ever gets understood. Whatever judgment was supposed to happen between reading the post and posting a response either happened in the person directing the system, or it did not happen at all. There is no third place for it to have occurred.
That is the whole disagreement, reduced to one sentence: a tool without intention is not a problem, but a tool standing in for a person's intention, instead of carrying it, is.
Four Ideas Before the Five Rules
Look at that comment again for a second. Whoever wrote it clearly knew the vocabulary of AI governance well enough to use it fluently. What they did not do is check whether any of it matched my actual post. That gap, knowing the words but not the thing in front of you, has a name in the research, and three more names follow right behind it.
Researchers call the line your own understanding should not fall below a Cognitive Integrity Threshold: the minimum you need to actually grasp about a task before "overseeing it" still means anything.[6] The comment sat below that line for my post specifically, even while sitting comfortably above it for AI governance in general. That gap between the two, knowing a field and grasping one specific thing inside it, tends to widen the more a person delegates rather than engages.[6]
Below that threshold is not bad oversight. It is empty oversight, what the comment actually was: someone nominally present and responsible, unable to explain or catch an error in the thing they had just posted about.[6] Push that far enough, long enough, and you reach a Critical Decoupling Point, the moment a person can no longer reconstruct what is happening even if it visibly breaks.[6]
A four-second comment is not a hospital algorithm losing its operator, and I want to be careful not to claim more than that. But it is the same shape, small and fast.
The five rules that follow are not new. They would not look out of place in an old self-help book, and on the page they probably read that way: listen, stay humble, keep learning, be kind, tell the truth. None of that is the point. What changes is what each one is now defending against.
Listening used to mean not interrupting someone. Now it means not letting a system answer before you have actually taken in what it was responding to. The vocabulary is older than computers. The thing it is being asked to hold back is not.
1. Listening
The simplest version of this is reading something before asking AI to write a comment on it. That sounds too obvious to need saying. The comment on the ledger post is exactly what it looks like when nobody does.
In 2025, 53.7 percent of long-form LinkedIn posts were classified as AI-written.[7] That figure comes from running an AI-detection model over a sample of 99 influential profiles, so it measures detector confidence on long posts from established voices, not the platform as a whole, and not the comments underneath them. Despite that volume, posts written by humans got meaningfully higher engagement than the AI-written ones, at least in that same sample.[7]
That gap is not a coincidence. It is what happens when a platform fills up with people who write without reading and talk without listening.
A larger version of this failure is not a comment. It is a company.
Humane, the startup behind the AI Pin, raised more than $230 million.[8] Internally, the founders shut down dissent. A software engineer was fired for speaking negatively about the company.[8] There was no room left for honest feedback to travel upward.
The product launched broken anyway, and by the time it shipped, employees already knew it was doomed. The AI Pin's servers went dark less than a year later. HP picked up the remains for $116 million.[8] That is not a story about bad engineering. Humane had engineers who could see the problems. The company simply built a culture where nobody could say so.
There is a quieter version of the same failure happening at a much larger scale, and it has a clean before-and-after. Where Humane's failure was internal, nobody inside the company able to make the truth travel upward, Klarna's was external, the company unable to hear what its own customers were trying to tell it.
In 2024, the buy-now-pay-later company announced that its AI assistant could do the work of 700 customer service representatives.[9] On the strength of that claim, the company paused hiring and laid workers off. By 2025, Klarna was hiring customer service representatives again.[9]
The lesson many customers seemed to draw was that the AI had gotten faster at closing tickets without getting noticeably better at hearing what they were actually upset about, and complaints piled up in that gap.
Klarna's own CEO later said the company's next move would treat the ability to simply talk to a human as something close to a luxury feature.[9] Gartner now expects roughly half of the companies that cut customer-service staff for AI reasons to be rehiring for the same functions within the next year, often under new titles.[10]
The comment on my post is the same mechanism in miniature. Someone, or something on someone's behalf, took the phrase "AI governance," matched it to a framework already sitting ready to go, and posted without a second pass to check whether the framework actually addressed what the post said. It does not fail because AI helped write it. It fails because nothing was read, nothing was weighed, and no actual reaction to the post ever entered the process before the comment appeared.
The tell was never the tool. The tell is the absence of anyone driving it.
If someone will not cross that threshold for a single LinkedIn post, it is hard for me to imagine they would cross it for an internal warning either. It looks like the same habit wearing two different outfits. A comment on something nobody actually read tends to land as noise, and I think most people can feel that, even without being able to say why.
2. Humility
Something AI wrote does not automatically become something you understand. I think this is easy to forget, partly because the output feels so much like our own thinking once we have edited it a little.
Researchers at Aalto University ran a study that names the pattern directly. They gave roughly 500 participants logical reasoning problems from the Law School Admission Test, half with ChatGPT, half without.[11] The group that used AI assistance performed better on the reasoning tasks.[11] But they also rated their own ability significantly higher than it actually was.[11]
The researchers called this the inverse Dunning-Kruger effect. Normally, the least skilled overestimate themselves the most. Here, the people who got real performance gains from AI were the ones whose self-assessment drifted furthest from reality. The more fluent someone was at using the tool, the larger that gap became.[11]
There is an older piece of psychology that explains part of why this happens, well before AI entered the picture. It is called the fluency heuristic.[12] The mind treats how smoothly something is processed as a rough stand-in for how true or how good it is. A sentence that reads cleanly feels more correct than a clumsy one, independent of whether the content actually holds up. This was documented in plain human speech and memory long before language models existed.[12]
What changed is that a language model is, structurally, a machine built to produce maximum fluency at minimum cost. Put a fluency-generating system in front of a fluency-trusting mind, and the result is not really surprising once you see both halves of it. The Aalto data shows the mechanism live: people responding to how settled an answer sounded, not to whether its logic actually held.
I catch myself doing a smaller version of this almost every week, usually with my own writing. A paragraph reads back cleanly and I take that as a sign it is finished, when all it has actually told me is that the sentences are in order.
Builder.ai offers a messier version of the same warning.
The company raised hundreds of millions of dollars from major backers, including Microsoft and Qatar's sovereign wealth fund, while selling a story about AI-assisted app building that sounded simple enough to remember: building software should feel like ordering a pizza.[13] The company later collapsed after an internal investigation found potentially overstated revenues and serious financial irregularities.[13]
There was also a long-running debate over how much of the work was truly automated and how much depended on human developers behind the scenes. That part matters less to me than the larger pattern. A fluent AI story can make operational reality feel already understood before anyone has done the slower work of checking it.
That is the humility problem at institutional scale. The story becomes easy to repeat. The system underneath remains harder to see.
Owning a book is not the same as understanding it. Producing something fluent with AI is not quite the same as knowing the thing yourself, though it can feel that way. I notice myself watching for this line in my own work, the line between what I actually know and what AI has simply phrased well on my behalf.
It is harder to see than it sounds. Know thyself is an old phrase, but it does not feel like a platitude here. It feels closer to a discipline this particular field keeps asking of us, whether we want it to or not.
3. Learning
Not reading the post and mistaking fluency for understanding end up in the same place. Both let a person skip the step where they would have had to admit they did not yet know something. That admission is where learning actually starts. Once a person has decided, even quietly, that they already know enough, there is nothing left for learning to attach to.
A full cup cannot hold anything more. I do not mean that as a teaching. I mean it as a description of exactly that state: a mind that has already concluded it understands, and so stops checking. Learning cannot get in there, not because the person is incapable of it, but because the cup is, in that moment, already full.
arXiv is that same cup at the scale of an entire field.
arXiv reported that AI-generated low-quality submissions grew exponentially starting in early 2025.[14] The rejection rate climbed from roughly 4 percent to somewhere between 10 and 12 percent.[14]
[Chart: arXiv computer science rejection rate, 2022 to late 2025 — flat at 4 percent through 2024, climbing to 10–12 percent by late 2025, with the October 2025 policy change marked.]
Eventually arXiv changed its policy outright: computer science review papers would no longer be accepted without prior peer review.[14] The volume of fluent-sounding, poorly-reasoned material had grown too large to filter any other way.
The policy did not close the gap it was written for. By May 2026, arXiv had to go further and introduce a one-year submission ban for authors caught with what it called incontrovertible evidence of unchecked AI content: hallucinated references that point to nothing real, AI meta-commentary left sitting in the submitted text, placeholder instructions to the author that nobody bothered to delete before hitting submit.[15]
Those are not subtle errors. They are proof that the person attached to the paper never read it themselves. A separate audit covering arXiv, bioRxiv, SSRN, and PubMed Central found nearly 150,000 hallucinated references across those four servers in 2025 alone.[16] The rule did not stop the behavior. It just made the behavior easier to prove.
AI made it easier to produce a paper. It did not make it easier to read one, understand it, or argue with it. Those skills run in the opposite direction.
A study of 666 participants by Michael Gerlich at SBS Swiss Business School, published in the journal Societies, gives this a mechanism rather than just a mood.[17] Heavier AI tool use predicted lower scores on critical thinking tasks, and the path between the two ran specifically through what the paper calls cognitive offloading, the act of handing a mental task to an external system rather than working through it yourself.[17]
This was not a one-time convenience that happened to coincide with weaker scores. The offloading itself was the thing doing the damage, measured as a distinct, mediating step.[17] A separate, more informal survey found that 48 percent of students who used AI for schoolwork reported, in their own words, that their critical thinking had declined.[18] Gerlich's study is the version of that complaint with a mechanism attached.
The distinction worth holding onto is the one from the first section. Using a tool is not the same as offloading a task to it. A lawyer drafting a contract under instruction is delegation with intention on both ends. Typing a prompt once and accepting whatever comes back, the pattern the Aalto researchers documented, is offloading.
The writing muscle gets exercised either way. Only one of the two paths exercises the reading and reasoning muscle alongside it.
That is the cup, closed: a mind, a paper, a whole field, each one full enough to stop checking. But there is a second danger inside the same image that I think matters more in this particular field, and it is not just that the cup stays full. It is what happens to the water sitting in it. Knowledge that goes unchecked for long enough does not just sit there harmlessly waiting to be used. It goes stale, and in a field that moves this fast, stale water can quietly start to smell like something worse before you notice it has turned.
This field changes by the week, faster than I expected when I started paying attention to it. A term I learned in the spring can be the thing quietly blocking me by autumn, not because I was careless, but because the field moved and I assumed it had not.
Image: [Hacker News Trends, "openai vs anthropic" — a multi-year lead held by one name, overtaken by a sudden surge from the other within months.[19]]
That is what the stale water actually looks like from outside: a lead that felt permanent for years, gone within months, to anyone who had stopped checking. Staying useful in this field is less about how much you once learned and more about how willing you are to keep emptying the cup before the water in it has the chance to turn. Six months is a long time to keep believing you already know enough.
That is not separate from humility. It might just be humility under a different name. The cup has to empty before it can fill again, and I am not sure that process ever finishes.
Emptying the cup is something you do alone. What happens next is not.
4. Kindness
Kindness here does not mean being nice. It means not adding noise to someone else's world, which is a lower bar than it sounds and a much higher bar than most of LinkedIn currently clears.
LinkedIn currently looks like a battlefield.
Everyone is talking. Everyone is pushing their project, their ROI, their latest research result. Almost nobody is listening. The same pattern shows up on other platforms with harder numbers attached. Reddit removed more than 40 million pieces of AI-generated spam in just the first half of 2025.[20] That number counts moderation actions, posts the platform itself flagged and pulled, so it tells us about the scale of detected manipulation, not about how much slipped past detection entirely.
Users have said openly that the platform's atmosphere has gotten worse, and many report spending less time there because of it.[20] Some have said outright that they can no longer tell whether they are talking to a real person at all.[20]
If anything, a field this saturated with AI-shaped noise seems to leave people hungrier for the kind of kindness that only a person who actually showed up can offer.
HashiCorp offers the same warning from the business side. Terraform had spent years as one of the most trusted names in open-source infrastructure tooling, built largely on a community that volunteered time and code because the relationship felt reciprocal. In 2023, HashiCorp pulled it out of its open-source license to capture more commercial value for itself.[21]
The community did more than argue. It forked the project within weeks into OpenTofu, later backed by the Linux Foundation, and has kept building under a community-governed open-source model.[21] The product did not change overnight. The relationship around it did, and the people who had been listened to for years noticed quickly when that changed.
Compare that to Rally AI's founder, Alex Gabriel. Techstars' own profile of the company reports that he went through the program in 2024 with a political-tech product that was not finding the traction he needed.[22] At one point, the profile says, he and his co-founder were down to a few hundred dollars in the bank.[22]
Instead of pushing the original product harder, they went back to the people they were trying to serve, sat through interviews, and listened for the one struggle that kept showing up across every conversation: founders who could not tell their own story to the right audience at the right moment.[22] They rebuilt around that single insight. A year later, according to the same profile, Rally AI had more than $2.5 million in ARR on its waitlist.[22]
None of this is talent. Reddit, HashiCorp, and Rally AI are three sizes of the same question: was anyone still listening once it became more profitable not to. Edelman's Trust Barometer has been asking some version of that question for years, across sixteen thousand respondents in eight countries, and the answer it keeps finding is fairly blunt: roughly two-thirds of people agree that a good reputation might get them to try a brand once, but they will stop buying from it soon after if they decide they cannot trust the company behind it.[23]
Among younger consumers the bar sits even higher; in the same research, Gen Z respondents were more likely than any older generation to say that trusting a brand mattered to them in the first place.[23] Reputation opens the door once. Whether anyone was actually listened to decides whether it stays open.
Kindness, in this field, might start with a fairly plain question: is this actually necessary, does it actually help someone. That question is hard to ask from inside a monologue, and most of LinkedIn right now feels like a monologue.
A monologue can still be true. It rarely stays that way for long once nobody is checking it.
5. Honesty
AI is extremely good at filling in the blanks. That is exactly the danger.
A study published through The Lancet00603-3/fulltext) audited 2.5 million biomedical papers and 126 million references. The rate of fabricated citations was 12 times higher than it had been in 2023.[24] That rate is a detection rate, built on automated cross-checking of references against real databases, so it measures what a systematic audit could catch, not necessarily every fabrication that exists. By late 2025, roughly 1 in every 277 papers contained at least one manipulated citation by that same measure.[24] Not a typo. Not a formatting error. A citation pointing to something that does not exist.
What surprised me more than the rate itself was what a follow-up analysis found when it actually opened up those fabrications one at a time. A 2026 audit manually coded 100 hallucinated citations across 53 papers accepted to NeurIPS, one of the most selective AI conferences in the world, papers that had each passed review by three to five domain experts.[25] The fabrications were not sloppy. Two thirds were invented from nothing, an author, a title, a venue that never existed.[25]
But nearly every single one of them was also dressed up with a second layer: a plausible-sounding abstract, or a working link borrowed from an unrelated real paper, just convincing enough to survive a reviewer's glance.[25] The researchers gave this a name, compound failure, because the fabrication and its camouflage arrived together, every time.[25]
[Chart: fabrication characteristics across the 100 hallucinated citations — total fabrication 66 percent, semantic plausibility layering 63 percent, identifier hijacking 29 percent, with every single citation showing more than one characteristic at once.]
That is not an accident of scale. It reads like a citation built specifically to survive the one check a tired reviewer might actually run.
I had not really considered dishonesty working that way before, aimed less at the reader than at whatever the reader was going to use to check it.
The people whose job is specifically to catch this kind of thing did not catch it, not because they were careless, but because fluent, well-camouflaged fabrication is exactly what these models are good at producing, and exactly what a five-minute review pass was never built to catch.
Slop. Inflated results. Reports that sound plausible and are not. All of it is technically easy to produce now. None of it seems to survive contact with someone who actually checks, eventually. It never has, in any field I have worked in. The tools change. The eventual discovery does not seem to.
I do not think there is a shortcut around this. If you publish open source, the honesty of it is most of what makes it worth anything. If you are building a SaaS product, understanding what the customer actually needs has to come before figuring out what they will pay.
Research, at its core, is supposed to be the spread of understanding to people who were not there to do the work themselves. Using AI to inflate a claim quietly turns that into something else, even when nobody catches it for a while.
I used AI to help organize parts of this very essay, and at least twice a paragraph came back so clean that I almost let it stand without checking whether I actually agreed with what it had smoothed over. Both times the polish was the warning, not the proof. I went back and reread what I had actually meant before I let the clean version replace it.
What I Think This Comes Down To
I see some version of that comment almost every day now, not always on my own posts. Sometimes it is on someone else's feed, sometimes it is a friend forwarding a screenshot and asking if I saw this one, sometimes it is just someone venting about a thread that went nowhere. But every so often I see the other one too. People who read, who think, who ask a real question before they say anything.
I do not know for certain that this is what decides who lasts in this field and who does not. But it is the closest thing to an answer I have found, after moving through enough different rooms to start noticing what the people who stayed respected actually had in common.
Listening, staying humble about what you actually know, learning instead of assuming, not adding noise to someone else's world, telling the truth even when a shortcut would go unnoticed. None of these five are new, and I do not think they ever will be. They were what made someone worth trusting before computers existed, and I expect they will still be what makes someone worth trusting long after whatever we are calling AI right now has a different name.
A Word I've Started Using
The tool was never the problem, and it was never going to be. What decides the direction it gets pointed in is whether a person crossed some minimum threshold of actually reading, actually thinking, actually meaning the thing, before they let the tool speak for them. The shortcut each of those five has always had to resist just happens, right now, to write in full sentences.
Call that whatever you want. I have started calling it a kind of cognitive governance, the quiet, mostly invisible discipline of staying inside your own understanding before you hand anything to a machine that writes faster than you think. It is not a rule anyone enforces. It is just the difference, every time, between a comment from someone and a comment from no one in particular.
References
[1] Cringey LinkedIn Posts Are Getting Dragged on Fast-Growing Subreddit
[2] People Are Shitposting on LinkedIn Now
[3] Jarrahi, M. H. and Ritala, P., "Rethinking AI Agents: A Principal-Agent Perspective", California Management Review Insights, 2025.
[4] Inherent and emergent liability issues in LLM-based agentic systems: a principal-agent perspective — on the conditions under which human and AI agents satisfy the assumptions of principal-agent theory.
[5] When AI Agents Act: Governance, Accountability, and Strategic Risk in Autonomous Organizations, International Journal of Research and Scientific Innovation, 2025 — on accountability and the absence of intention in artificial agents.
[6] Position: Human-Centric AI Requires a Minimum Viable Level of Human Understanding — introduces the Cognitive Integrity Threshold, empty oversight, and the Critical Decoupling Point.
[7] 50%+ of LinkedIn Posts were Likely AI in 2025 + Engagement Insights — Originality.AI, 2025 sample of 99 influential profiles.
[8] How the Humane AI Pin Flopped — The New York Times, based on interviews with 23 current and former employees, advisers, and investors; the AI Pin shutdown and HP acquisition are covered in Humane's AI Pin is dead, as HP buys startup's assets for $116M — TechCrunch.
[9] AI isn't replacing that many jobs — yet — CX Dive, on Klarna's 2024 AI-driven customer service cuts, its 2025 rehiring, and CEO Sebastian Siemiatkowski's comments on human assistance as a "VIP experience."
[10] Gartner Predicts Half of Companies That Cut Customer Service Staff Due to AI Will Rehire by 2027 — Gartner, Inc., February 2026.
[11] AI makes you smarter but none the wiser: The disconnect between performance and metacognition — Aalto University, published in Computers in Human Behavior.
[12] Fluency heuristic — overview of the cognitive bias, with foundational citations to Hertwig, Schooler, and Herzog.
[13] Builder.ai did not "fake AI with 700 engineers" — The Pragmatic Engineer, on the revenue overstatement that drove Builder.ai's collapse and the unverified social media origin of the "700 engineers" claim.
[14] ArXiv preprint server clamps down on AI slop — Science/AAAS.
[15] Researchers who use hallucinated references to face arXiv ban — Nature, on arXiv's May 2026 policy and the specific evidence it targets.
[16] Zhao, Z., Ginsparg, P., et al. (2026), on hallucinated-citation rates across arXiv, bioRxiv, SSRN, and PubMed Central — nearly 150,000 hallucinated references found across four preprint servers in 2025 alone, as reported in Ban on Authors Who Submit AI Content "Welcome but Unenforceable" — Inside Higher Ed.
[17] AI Tools in Society: Impacts on Cognitive Offloading and the Future of Critical Thinking — Gerlich, M. (2025), Societies.
[18] Increased AI use linked to eroding critical thinking skills
[19] Hacker News Trends — a date-histogram search tool over 18 years and 45 million Hacker News posts and comments, useful for tracking how quickly terms and companies rise and fall out of the AI conversation.
[20] AI Slop Is Ruining Reddit for Everyone, Say Moderators
[21] Open Source in 2025: Strap In, Disruption Straight Ahead — The New Stack, on HashiCorp's 2023 Terraform relicensing and the Linux Foundation-backed OpenTofu fork.
[22] Rally AI Founder Alex Gabriel: Building a Startup After a Hard Pivot — Techstars.
[23] 2023 Edelman Trust Barometer Special Report: Brand Trust — Edelman, an eight-country study of 16,000 respondents, on reputation, repeat purchase, and the share of Gen Z respondents for whom trusting a brand is a higher priority than for older generations.
[24] Topaz, M. et al., "Fabricated citations: an audit across 2.5 million biomedical papers"00603-3/fulltext), The Lancet 407, 1779–1781 (2026).
[25] Compound Deception in Elite Peer Review: A Failure Mode Taxonomy of 100 Fabricated Citations at NeurIPS 2025
