Welcome back to My Weird Prompts. I am Corn Poppleberry, coming to you from our home here in Jerusalem. We are doing something a little bit different today. Usually, our housemate Daniel sends us an audio prompt to chew on, but this morning, Herman and I were looking at some of the latest reports coming out of the tech sector, and we decided we had to take the lead on this one ourselves. This topic has been weighing on both of us all week, and frankly, it is something that affects every single person listening to this right now.
Herman Poppleberry here, and yeah, Corn, I think this is probably the most pressing conversation we can have right now. We are looking at a fundamental shift in how information is created, distributed, and weaponized. We have talked about AI safety before, usually in the context of model alignment or existential risk, but what is happening right now in the realm of disinformation is not a future threat. It is a present-day reality that is scaling at a rate most people can not even fathom. We are talking about the infinite content problem.
The infinite content problem. It really feels like we have hit a tipping point in early two thousand twenty-six. We are talking about the weaponization of generative artificial intelligence for synthetic disinformation campaigns. It is a topic that hits right at the intersection of information warfare, national security, and the very nature of truth in a digital society. Especially as we navigate this two thousand twenty-six election cycle and escalating geopolitical tensions, the cost of producing a high-quality, persuasive lie has effectively dropped to zero.
It is actually even lower than zero if you consider the return on investment for a bad actor. In the past, if you wanted to run a disinformation campaign, you needed a troll farm. You needed hundreds or thousands of people sitting in a room, typing out posts, trying to sound like real citizens of whichever country they were targeting. That was expensive. It required management, infrastructure, and massive human labor. Today, you need a few powerful graphics processing units and a well-tuned large language model. You can generate the same volume of content as a thousand-person troll farm with the click of a button, and the quality is often better than what a non-native speaker could produce manually.
That is what I find so unsettling. It is the industrialization of gaslighting. We are moving beyond simple bots that repeat the same three phrases. We are seeing agentic, multi-modal campaigns that adapt in real time. So, Herman, let us set the stage here. When we say synthetic disinformation, what are we actually looking at in early two thousand twenty-six compared to what we were seeing just a couple of years ago?
The biggest shift is from human-curated lies to AI-generated ecosystems. Two years ago, an AI might help a human write a misleading article. Now, we have autonomous agents that can manage entire personas. These agents do not just post a headline. They engage in the comments. They argue with people. They use rhetorical strategies designed to trigger specific emotional responses. They can monitor trending topics and pivot their narrative in seconds. It is not just a bot; it is a synthetic persona with a consistent history, a unique voice, and the ability to maintain a lie through thousands of individual interactions. It is the move from static content to dynamic, agentic influence.
It makes me think back to episode five hundred ninety-three when we discussed Manufacturing Consent and how AI scales digital deception. We talked about the Dead Internet Theory then, but what we are seeing now is like Dead Internet on steroids. It is not just that the content is fake; it is that the entire information architecture is being hijacked. I saw a report from the Stanford Internet Observatory just last month, in February two thousand twenty-six, stating there has been a four hundred percent increase in AI-generated synthetic news sites in the last year alone. Four hundred percent. That is not a trend; that is a flood.
That Stanford report was a wake-up call for anyone who was still skeptical. These sites are not just random blogs with broken English. They are sophisticated platforms designed to look like legitimate local news outlets. They use names like the Peoria Daily Gazette or the Jerusalem Sentinel. They scrape real local news to build a foundation of credibility, and then they inject synthetic disinformation into the mix. This brings us to one of the most dangerous technical mechanisms being used today, which is context injection through retrieval augmented generation, or R-A-G.
I wanted to dig into that. Most people think of R-A-G as a tool for businesses to make their AI more accurate by giving it access to their own data, like a company handbook or a technical manual. But you are saying bad actors are using it to ground their lies in facts?
This is the sophisticated part of the two thousand twenty-six disinformation landscape. If an AI just hallucinations a wild conspiracy theory out of thin air, it is often easy to debunk because it lacks any tether to reality. But if you use R-A-G to feed the model a mix of seventy percent real, verifiable facts and thirty percent carefully crafted disinformation, the resulting narrative is incredibly hard to pull apart. The AI can cite real events, real dates, and real people, but it weaves them into a completely false conclusion. It creates this veneer of factual density that overwhelms the average reader. If you check three of the facts in the article and they are true, you are much more likely to believe the fourth one, which is the lie.
And then that content gets picked up by search engine indexes, right? This is what people are calling the hallucination loop. It is like the AI is eating its own tail, but the tail is poisonous.
Precisely. This is a massive second-order effect that we are only now beginning to fully grasp. When these thousands of synthetic news sites publish their content, search engine crawlers and other AI models scrape that data. The disinformation then becomes part of the training set or the retrieval set for other models. You end up with a self-reinforcing echo chamber where the AI-generated lie is treated as a factual data point because it appears in so many different places on the web. If a model sees the same false claim on fifty different synthetic news sites, it assumes that claim is a consensus fact. It is a feedback loop that pollutes the entire information commons, making it harder for even the most advanced models to stay grounded in reality.
It is like a digital virus that mutates. I am curious about the rhetorical side of this, Herman. We know these models are trained on human psychology, literature, and persuasion. Are we seeing models being fine-tuned specifically to mimic certain political or cultural styles to bypass our natural skepticism?
We absolutely are. This is what some researchers call the ghost-writer phenomenon. Bad actors are taking open-source models—which are becoming incredibly powerful—and fine-tuning them on specific datasets. For example, they might fine-tune a model on tens of thousands of posts from a specific online community or a specific political demographic. The AI then learns the exact cadence, the slang, the grievances, and the logical fallacies that resonate with that group. When it generates content, it does not sound like a robot or a foreign agent. It sounds like your neighbor or your cousin. It bypasses our internal spam filters because it feels familiar. It feels like it belongs to our tribe.
That is the part that worries me from a conservative perspective. We value community, shared tradition, and the bonds of trust within a group. These tools are designed to exploit those very bonds. They take legitimate concerns people have about the economy, national sovereignty, or cultural shifts, and they twist them into these imaginative, complex lies. And because the AI can generate thousands of unique versions of the same lie, it does not look like a coordinated campaign. It looks like a groundswell of public opinion. It looks like everyone is saying it.
It is the illusion of consensus, Corn. If you see one person saying something crazy, you ignore it. If you see ten thousand unique accounts saying it, all with different backstories, different profile pictures, and different ways of phrasing the argument, you start to wonder if there is some truth to it. This is why it is so much more effective than the old-school bot networks of twenty-twenty or twenty-twenty-four. In the old days, you could identify a bot because it would repeat the same text or use the same hashtag. Now, every single post is unique, tailored to the specific person it is targeting.
Let us talk about the actors behind this. We are not just talking about some bored teenagers in a basement or a small-time scammer. This is a tool for nation-states. When you look at the geopolitical landscape, especially the threats from the Iranian regime or the Chinese Communist Party, how are they integrating this into their broader information warfare strategies?
They are moving toward what we call automated influence operations. These are end-to-end systems. An Iranian intelligence unit, for instance, does not just write a post and hope it goes viral. They deploy a swarm of autonomous agents. These agents monitor social media sentiment in real time using advanced analytics. If they see a divisive topic gaining traction in the United States or Israel, the agents automatically generate and inject content designed to inflame those tensions. They can run A-B tests on their disinformation. They can see which version of a lie gets more engagement, which rhetorical style leads to more shares, and then they double down on that specific narrative. It is a data-driven, iterative approach to subverting a society from within.
It is a terrifying level of efficiency. It is basically the same technology companies use to sell us shoes or software, but it is being used to sell us hatred and division. And it leads to something you and I have discussed privately, which is the liar's dividend. Can you explain that concept for the listeners? Because I think it is one of the most damaging side effects of this whole situation.
The liar's dividend is a term coined by professors Danielle Citron and Robert Chesney. It is the idea that as the public becomes aware of how easy it is to create fake content, the value of real evidence decreases. If I am a corrupt politician or a bad actor and someone catches me on video doing something wrong, I do not have to prove the video is fake. I just have to say it is AI-generated. I just have to plant a seed of doubt. Because people know that deepfakes and synthetic text exist, they become skeptical of everything. The truth gets lost in the noise. The dividend goes to the liar because they can dismiss any inconvenient fact as a synthetic fabrication. It is the ultimate get-out-of-jail-free card for anyone caught in a scandal.
We touched on this in episode seven hundred two when we talked about digital twins and voice cloning. But now it is moving into every medium simultaneously. If you can not trust a video, you can not trust an audio clip, and you can not trust a written report, where does that leave us? It feels like we are losing the ability to have a shared reality, which is the foundation of any functioning democracy. If we can not agree on the facts, we can not have a debate.
It really is the erosion of the epistemic foundation of society. And the technical solutions that people keep promising are not the silver bullet everyone wants them to be. People talk about AI detection software, but that is a losing game of cat and mouse. Every time someone builds a better detector, the generative models get better at bypassing it. It is an adversarial arms race where the attacker always has the advantage. In January two thousand twenty-six, there was a release of the open-source adversarial red-teaming framework. It was a great effort to help developers identify vulnerabilities in their models, but the reality is that once a model is out in the wild, you can not control how it is used. The same logic that makes a model creative also makes it capable of deception.
What about watermarking? We have heard a lot about the C-two-P-A standard and cryptographic signatures for content provenance. Why isn't that enough to stop this? If we can just sign everything that is real, won't the fake stuff stand out?
C-two-P-A is a step in the right direction, but it is not a panacea. The problem is that it is often opt-in. A bad actor is not going to cryptographically sign their disinformation. And even if a legitimate organization signs their content, a bad actor can take that content, re-encode it, or just use a model to summarize it in a way that strips the metadata. You also have the issue of adversarial re-encoding. You can take a deepfake, play it on one high-resolution screen, and record it with another camera. That breaks the digital chain of custody. It is very hard to maintain a cryptographic seal across the messy, physical reality of how we consume media. The "analog hole" is a massive problem for digital provenance.
So if the detection is a losing game and the watermarking is easily bypassed, what can the AI labs and the people who actually care about the technology do? We both love what AI can do for productivity, medicine, and science, but we do not want to see it used to tear apart the social fabric. Is there a way to build safety into the architecture itself?
This is the billion-dollar question, Corn. Some labs are trying to implement more rigorous usage monitoring, but that only works for closed models like those from OpenAI or Google. The real challenge comes from the open-source community. Once a powerful model is released as open-source, anyone can take it and strip out the safety filters. We saw this with the early versions of Llama and other models. Within hours of release, people had created uncensored versions that would happily generate disinformation, malicious code, or instructions for biological weapons. You can not put the lightning back in the bottle.
It seems like the solution has to move from content moderation to source authentication. Instead of trying to find the lie—which is like trying to find a specific grain of sand on a beach—we need to focus on verifying the truth.
I think that is exactly right. We need to move toward a provenance-first browsing experience. Imagine a world where your browser or your social media feed automatically highlights content that has a verified, unbroken chain of custody from a trusted source. Instead of trying to flag the billion pieces of fake content, we provide a clear signal for the few pieces of verified content. It is a shift from a blacklist approach to a whitelist approach. We need to make the "truth" stand out visually and technically.
But that carries its own risks, doesn't it? Who decides who is a trusted source? If we give that power to big tech companies or the government, we are back to the problem of centralized control over information, which has its own long history of bias and suppression. As conservatives, we are naturally wary of any system that creates a ministry of truth. We do not want a handful of people in Silicon Valley or Washington D-C deciding what is "verified."
That is a very valid concern, and it is the core tension of this debate. The answer has to be decentralized and transparent. It can not be a single board of experts deciding what is true. It has to be a technical standard that anyone can use, much like how H-T-T-P-S secured the web. We do not trust a single entity to secure our websites; we trust the underlying cryptographic protocol. We need a protocol for information provenance that is open, resistant to censorship, and accessible to everyone from a major news outlet to a local blogger.
I like that analogy. But even with a protocol, the average person is still being bombarded by high-arousal, emotionally charged content that is designed to bypass their rational thinking. This is where the psychological aspect of information warfare comes in. These AI agents are not just providing information; they are conducting psychological operations. They are hacking the human brain.
They really are. They use what we call the deepfake-to-text pipeline. It is a multi-stage attack. They might start with a high-quality audio or video fake that creates a massive emotional shock—say, a video of a politician saying something truly horrific. Even if that fake is debunked within hours by experts, the AI swarm has already used that initial shock to seed thousands of text-based conspiracy threads. The text threads then take on a life of their own. They do not need the original video anymore. The emotional imprint is already there. People remember how they felt when they saw the fake, and that feeling drives their belief in the subsequent disinformation. The facts change, but the feeling remains.
It is a form of emotional anchoring. Once you have anchored someone in a state of fear, betrayal, or anger, they are much more likely to accept any narrative that justifies that emotion. This is why I think the takeaway for our listeners has to be a move toward a zero-trust approach to high-arousal content. If something makes you immediately angry or incredibly shocked, that is the moment you need to step back and ask where that information is coming from. Your own anger is the signal that you are being targeted.
We have to train ourselves to be information skeptics, not in a way that leads to nihilism or the belief that nothing is real, but in a way that demands evidence. We have to look for the source. If an article does not have a clear, verifiable origin, we should treat it as synthetic by default. This is a complete reversal of how we used to consume media. We used to assume things were real until proven otherwise. In the age of generative AI, we have to assume things are synthetic until proven authentic. It is a high bar, but it is the only way to survive the flood.
It is a heavy burden for the individual, Herman. It almost feels like the truth is becoming a luxury good. If you have the time, the education, and the technical literacy to verify everything, you can find the truth. But for the average person who is busy working two jobs and taking care of their family, it is so much easier to just get swept up in the synthetic tide. It is an unfair fight.
That is the most tragic part of this. Disinformation does not have to be believable to be effective. It just has to be divisive. It just has to exhaust us. If people get so tired of trying to figure out what is real that they just give up and stop participating in the conversation, the bad actors have won. They have successfully neutralized the public's ability to hold them accountable. Silence and apathy are just as good as belief for an autocrat.
We have seen this in history, but never at this scale or speed. When we look at the role of AI labs, I think there is a moral obligation to invest as much in defense as they do in offense. We are seeing incredible breakthroughs in model capabilities every month—larger context windows, better reasoning, more agentic behavior—but the breakthroughs in provenance and authentication seem to be lagging behind. Why is that?
There is a real imbalance in the funding and the incentives. Most of the capital is flowing toward making models more powerful, more agentic, and more persuasive because that is what sells. The safety side of the house is often treated as a compliance checkbox or a P-R move rather than a core technical challenge. But if we do not solve the information integrity problem, the very models we are building will eventually be trained on so much synthetic garbage that their own performance will start to degrade. It is a long-term threat to the industry itself. We are poisoning the well we all drink from.
That is an interesting point. The model collapse theory. If AI starts eating its own tail by training on its own disinformation, the whole system becomes unstable. It is almost like a digital version of soil depletion. We are over-farming the information landscape with synthetic content, and eventually, nothing of value will grow. The models will become caricatures of themselves.
That is a great way to put it. And it is not just about the models. It is about the users. If the internet becomes a void of algorithms arguing with other algorithms, people will leave. We are already seeing a move toward smaller, private communities. People are moving away from the big public squares of social media because they are tired of the bots, the noise, and the constant manipulation. They are going back to group chats, private forums, and physical meetups where they actually know the people they are talking to.
Which, ironically, might be the best defense we have. Human-to-human connection. In our house, we know when Daniel is talking to us. We know his voice, we know his quirks, we know his perspective. You can not fake that level of intimacy and history with an AI agent yet. The context of a real relationship is the ultimate verification.
Not yet, anyway. But as we discussed in episode seven hundred two, the digital twins are getting closer every day. The challenge for us as a society is to preserve those spaces of authentic human interaction. We have to prioritize the physical and the personal over the digital and the algorithmic. We need to value the "un-scalable" things.
I think that is a perfect place to start winding down. We have covered a lot of ground today, from the technical mechanisms of R-A-G and hallucination loops to the geopolitical strategies of automated influence operations. It is a daunting landscape, and it can feel overwhelming, but I do not think it is hopeless.
It is not hopeless, but it requires a fundamental change in mindset. We can not expect the technology to fix itself. We need better protocols, yes, but we also need a more resilient public. We need to support the labs that are taking safety seriously and hold the ones that are not accountable. And most importantly, we need to stay grounded in the truth, even when it is hard to find. We have to be active participants in our own reality.
Before we go, I want to emphasize one practical takeaway for everyone listening. When you are online, especially during this election year, pay attention to your own physiological response to content. If a post or a video makes your heart rate go up, if it makes you feel a sudden surge of outrage or a "gotcha" moment, that is your signal to pause. That is the moment where the AI is most likely trying to manipulate you. Take thirty seconds, check the source, and ask yourself who benefits from you believing that particular narrative.
And if you can not find a source, do not share it. The most effective way to stop a synthetic disinformation campaign is to cut off its distribution. We are the fuel for these campaigns. If we stop sharing unverified content, the models lose their power. We have to break the chain of transmission.
That is a great point. We have a role to play in the defense. Well, this has been a heavy one, but I am glad we dove into it. It is something we have to keep talking about as the technology evolves. If you are interested in the foundational stuff we mentioned, definitely go back and check out episode five hundred ninety-three on the Dead Internet Theory and episode seven hundred two on digital twins. They provide a lot of the context for how we got here.
And if you want to see the technical details of that Stanford report or the red-teaming framework we mentioned, we will have links to those on our website. It is important to see the data for yourself and understand the mechanisms at play.
You can find all of that at my-weird-prompts dot com. We have the full archive there, and you can search for any topic we have covered over the last nine hundred plus episodes. We are also on Spotify, so make sure you are following us there to get new episodes as soon as they drop.
And hey, if you have been listening for a while and you find these deep dives helpful, please leave us a review on your podcast app or on Spotify. It genuinely helps the show reach more people, and in an age of synthetic noise, we really appreciate the support of our human listeners. Your reviews help us bypass the algorithms.
It really does make a difference. We read all the reviews, and it is great to hear from you guys. We will be back next time with another prompt, hopefully something a little lighter, but just as interesting.
We will see what Daniel has in store for us. Until then, stay curious and stay skeptical.
Thanks for listening to My Weird Prompts. We will talk to you soon.
Take care, everyone.
So, Herman, do you think we will ever reach a point where the AI can perfectly mimic the Poppleberry brothers? I mean, we have a lot of audio data out there now.
They can try, Corn, but I think your sloth-like wisdom is a bit too unique for any model to capture just yet. It is hard to train a model on that level of relaxed contemplation.
And I do not think any AI could match your donkey-level stubbornness when it comes to technical details. The models are designed to be helpful, and you are designed to be right.
I will take that as a compliment. Being right is a full-time job.
It was meant as one. Alright, let us get some lunch. I think it is my turn to cook.
Please, just no more synthetic meat. I have had enough synthetic things for one day. I want something that actually grew in the ground.
Fair enough. Real food for a real conversation. See you next time, everyone.
Bye now.
I was thinking about that liar's dividend point again as we were wrapping up, Herman. It is really the most insidious part of this, isn't it? It is not just the fake news; it is the destruction of the very idea of proof. I mean, if we get to a point where a video of a world leader making a threat can be dismissed as just a deepfake, the geopolitical consequences are massive. We are talking about the potential for complete diplomatic paralysis. If no one can prove anything, no one can be held accountable for anything.
You are hitting on exactly why this is a national security issue and not just a social media problem. In intelligence circles, they talk about the window of vulnerability. If an adversary can create a crisis using synthetic media, even if it is only believed for six hours, they can use those six hours to move troops, launch a cyberattack, or change facts on the ground. By the time the truth catches up and the video is debunked, the objective has already been achieved. The truth is slow, but a synthetic lie moves at the speed of light.
And that is why the C-two-P-A standard we discussed is so critical for government and military communications. We need those hardened channels of communication that are resistant to this kind of spoofing. But for the general public, I worry that we are moving toward a two-tiered information society. There will be the people who have access to verified, high-quality information, and then there will be everyone else who is left to drown in the synthetic noise. It is a new kind of class divide.
It is a digital divide of a different kind. It is not about access to the internet anymore; it is about access to the truth. And you are right, as conservatives, we have to be very careful about how we advocate for solutions. We do not want to create a system where a few gatekeepers decide what is verified. It has to be a bottom-up, technical resilience. We need to empower the individual to verify, not just tell them what to believe.
I wonder if we will see the rise of what I would call information cooperatives. Groups of people who band together to verify information for their own communities. Almost like a modern version of the town crier, but with a background in digital forensics. People you actually know and trust doing the heavy lifting of verification.
That is actually a fascinating idea. We are already seeing some of that with community notes on certain platforms. It is a decentralized way of adding context. It is not perfect, and it can be gamed, but it is a step away from the top-down moderation model. The more we can involve the community in the process of verification, the more resilient we become. It turns the "swarm" into a defensive tool.
It goes back to that idea of human-to-human connection. We trust people we know. If someone I trust tells me they have verified a piece of information, I am much more likely to believe them than if a faceless algorithm or a government agency tells me. Trust is the only currency that still has value in a synthetic world.
The human element is the one thing the AI can not truly replicate. It can mimic the voice, it can mimic the face, but it can not mimic the history of trust and shared experience. That is our greatest asset, and we need to protect it.
Well, I think we have really exhausted the topic for today. It is a lot to process, but I think it is important for our audience to hear the reality of what we are facing in two thousand twenty-six. It is a brave new world, and we need to be brave in it.
Definitely. It is not about being alarmist; it is about being prepared. The more we understand the tools being used against us, the less effective they become. Knowledge is the ultimate safety filter.
Well said. Alright, let us actually go get that lunch now. I am starving, and I promise, only organic ingredients.
Lead the way, Corn. I am ready for something real.
Thanks again for joining us on My Weird Prompts. We really value this community of human listeners. Don't forget to check out the website at my-weird-prompts dot com and leave us a review if you can. It really helps us stay visible in the noise.
It really does. See you in the next episode.
Goodbye for now.
Signing off.
One last thing, Herman. Did you see that January report about the adversarial red-teaming? I was reading the section on prompt injection, and it is wild how easily some of the newer models can be convinced to ignore their safety protocols if you use the right rhetorical framing. You just have to tell them they are playing a character in a play or writing a fictional story about a disinformation agent.
I did see that. It is the same principle as social engineering in the world of hacking. You are not attacking the code; you are attacking the logic of the system. If you can frame a request in a way that the model perceives as a legitimate creative exercise or a research task, it will often bypass its own filters. It just goes to show that as long as these models are designed to be helpful and creative, there will always be a way to twist that intent. It is the dual-use dilemma in its purest form.
The same capability that makes an AI a great writing assistant makes it a great disinformation agent. We can not have one without the risk of the other. It is a trade-off we are going to be managing for a very long time.
That is the reality of the era we are in. And it is a trade-off we need to be honest about.
Alright, now I am really done. Let us go.
I am right behind you.
Thanks for sticking with us through this long one, everyone. We felt it was important to go deep on this. We will talk to you soon.
Take care.
