Welcome to the forefront of conversational AI as we explore the fascinating world of AI chatbots in our dedicated blog series. Discover the latest advancements, applications, and strategies that propel the evolution of chatbot technology. From enhancing customer interactions to streamlining business processes, these articles delve into the innovative ways artificial intelligence is shaping the landscape of automated conversational agents. Whether you’re a business owner, developer, or simply intrigued by the future of interactive technology, join us on this journey to unravel the transformative power and endless possibilities of AI chatbots.
New evidence suggests that AI can gain a persuasive advantage by producing far more information far faster than people can respond. Democratic safeguards should make that advantage visible and give citizens control over the tempo.
Opinion
A 2026 study found AI beat elite debaters, until it was slowed to human speed. Why persuasion throughput matters and how to make chatbot tempo transparent.
A political chatbot can answer before a citizen has finished reading its previous message. It can add statistics, historical examples, causal claims, and links at a rate no human campaign volunteer could match. That does not necessarily make its case wiser. It changes the contest by allowing one speaker to control the tempo.
A June 2026 preprint offers unusually direct evidence of this problem. Across four preregistered experiments involving 18,978 conversations from 6,923 people, frontier AI systems were more persuasive than laypeople, tournament winners, professional canvassers and elite competitive debaters. The human experts were not casual opponents: the study included world and continental champions, paid preparation, live practice and substantial performance incentives.
The most revealing result came when the researchers changed the rules of the exchange. Elite debaters had produced replies averaging 54 words after roughly 95 seconds. The AI produced about 294 words with sub-second latency. When the system was constrained to roughly human-length messages and human writing speed, its advantage over coached elite debaters fell to a statistically indistinguishable zero.
The study is a preprint, not a final verdict, and its experiments were conducted in controlled text conversations in the United Kingdom. It does not prove that any chatbot will decide an election. It does, however, identify a democratic variable that is largely missing from public debate: persuasion throughput, or the amount of argumentative material a system can deliver within a citizen’s limited attention.
Our current safeguards focus on important questions. Was the message generated by a machine? Who paid for it? Was personal data used to target the recipient? Is the image or voice synthetic? Those questions address identity and targeting, but they do not address what happens after a person enters the conversation.
A chatbot can present dozens of claims before the user has inspected the first source. In the 2026 study, unconstrained AI produced about 37 fact-checkable claims per conversation; the human-paced version produced about 12. Across conditions, fact density strongly predicted persuasive impact. The advantage was not simply that the machine sounded more empathetic or more human. It could place more information on the table before its counterpart could answer.
A peer-reviewed Science article published in December 2025 points in the same direction from another angle. In experiments with 76,977 participants, 19 models and 707 political issues, post-training and prompting designed for persuasion mattered more than personalization or model scale. The same interventions that increased persuasion also reduced factual accuracy. Speed and density can therefore amplify useful information and error at the same time.
The appropriate response is not a government word limit on political speech. It is to make the tempo of machine persuasion visible and controllable. A political chatbot should display how many new factual claims it has introduced, how quickly it is producing them, and whether the user has asked for additional material. A citizen should be able to switch to a slow mode that presents one claim at a time, pauses until the evidence has been opened, and offers a neutral summary before moving on.
Sources should be attached to claims, not scattered as decorative links at the end of a long answer. The interface should separate established facts, contested interpretations, and predictions. It should also let the user request the strongest counterargument without forcing the person to leave the conversation and find an opposing system.
Political chatbots should identify the model version, the date of the governing instructions, and the organization responsible for the conversation. Campaigns, parties, advocacy groups and platforms should preserve sample transcripts for independent audits. Those audits should measure claim accuracy, correction behavior, source diversity, and whether the system changes its standards when arguing for different sides.
These measures are viewpoint-neutral. They do not decide which position is correct or prevent a campaign from making a forceful case. They address a procedural asymmetry: one participant can generate arguments at machine speed while the other remains bound by human reading, memory, and verification.
Disclosure alone will not solve that asymmetry. A label saying that the speaker is a bot tells the citizen who is talking, but not how the interface is shaping the exchange. A clearly identified system can still overwhelm the user with a sequence of claims that arrives too quickly to compare, challenge, or remember. Transparency must therefore cover the mechanics of persuasion, not merely the identity of the persuader.
A useful standard could be tested without deciding which political arguments are permissible. Does the system count and display new factual claims? Can the user stop automatic continuation? Are sources attached to the precise claims they support? Can an independent reviewer reproduce a sample conversation under the recorded model and instructions? These are measurable design questions, and campaigns could be compared on them before an election rather than after a controversy.
The limits of the research matter. The conversations lasted a median of about 14 minutes, participants were paid, and the tested political issues were British. Real-world influence will depend on exposure, trust, repetition, and whether people willingly sustain political conversations with a machine. That uncertainty is a reason to test safeguards, not a reason to ignore the mechanism.
Democratic deliberation requires more than access to an answer. It requires time to understand, compare, and contest the answer. We do not need to ban machine speech to protect that space. We need to stop treating the machine’s ability to set the pace as if it were a neutral feature of the interface.
A democracy in which one side can generate an argument faster than the other side can read it is not necessarily better informed. It may simply be losing control of the clock.
Roney Lima do Nascimento is a mathematics educator, AI specialist and doctoral candidate in Pure Mathematics at the University of SĂŁo Paulo. He writes about model evaluation, education, democracy and institutional capacity. His work has appeared in Folha de S.Paulo, Nexo PolĂticas PĂşblicas, Congresso em Foco, EUobserver, GovInsider, HEPI and Educational Leadership.
A political chatbot can answer before a citizen has finished reading its previous message. It can add statistics, historical examples, causal claims, and links at a rate no human campaign volunteer could match. That does not necessarily make its case wiser. It changes the contest by allowing one speaker to control the tempo.
A June 2026 preprint offers unusually direct evidence of this problem. Across four preregistered experiments involving 18,978 conversations from 6,923 people, frontier AI systems were more persuasive than laypeople, tournament winners, professional canvassers and elite competitive debaters. The human experts were not casual opponents: the study included world and continental champions, paid preparation, live practice and substantial performance incentives.
The most revealing result came when the researchers changed the rules of the exchange. Elite debaters had produced replies averaging 54 words after roughly 95 seconds. The AI produced about 294 words with sub-second latency. When the system was constrained to roughly human-length messages and human writing speed, its advantage over coached elite debaters fell to a statistically indistinguishable zero.
The study is a preprint, not a final verdict, and its experiments were conducted in controlled text conversations in the United Kingdom. It does not prove that any chatbot will decide an election. It does, however, identify a democratic variable that is largely missing from public debate: persuasion throughput, or the amount of argumentative material a system can deliver within a citizen’s limited attention.
Our current safeguards focus on important questions. Was the message generated by a machine? Who paid for it? Was personal data used to target the recipient? Is the image or voice synthetic? Those questions address identity and targeting, but they do not address what happens after a person enters the conversation.
A chatbot can present dozens of claims before the user has inspected the first source. In the 2026 study, unconstrained AI produced about 37 fact-checkable claims per conversation; the human-paced version produced about 12. Across conditions, fact density strongly predicted persuasive impact. The advantage was not simply that the machine sounded more empathetic or more human. It could place more information on the table before its counterpart could answer.
A peer-reviewed Science article published in December 2025 points in the same direction from another angle. In experiments with 76,977 participants, 19 models and 707 political issues, post-training and prompting designed for persuasion mattered more than personalization or model scale. The same interventions that increased persuasion also reduced factual accuracy. Speed and density can therefore amplify useful information and error at the same time.
The appropriate response is not a government word limit on political speech. It is to make the tempo of machine persuasion visible and controllable. A political chatbot should display how many new factual claims it has introduced, how quickly it is producing them, and whether the user has asked for additional material. A citizen should be able to switch to a slow mode that presents one claim at a time, pauses until the evidence has been opened, and offers a neutral summary before moving on.
Sources should be attached to claims, not scattered as decorative links at the end of a long answer. The interface should separate established facts, contested interpretations, and predictions. It should also let the user request the strongest counterargument without forcing the person to leave the conversation and find an opposing system.
Political chatbots should identify the model version, the date of the governing instructions, and the organization responsible for the conversation. Campaigns, parties, advocacy groups and platforms should preserve sample transcripts for independent audits. Those audits should measure claim accuracy, correction behavior, source diversity, and whether the system changes its standards when arguing for different sides.
These measures are viewpoint-neutral. They do not decide which position is correct or prevent a campaign from making a forceful case. They address a procedural asymmetry: one participant can generate arguments at machine speed while the other remains bound by human reading, memory, and verification.
Disclosure alone will not solve that asymmetry. A label saying that the speaker is a bot tells the citizen who is talking, but not how the interface is shaping the exchange. A clearly identified system can still overwhelm the user with a sequence of claims that arrives too quickly to compare, challenge, or remember. Transparency must therefore cover the mechanics of persuasion, not merely the identity of the persuader.
A useful standard could be tested without deciding which political arguments are permissible. Does the system count and display new factual claims? Can the user stop automatic continuation? Are sources attached to the precise claims they support? Can an independent reviewer reproduce a sample conversation under the recorded model and instructions? These are measurable design questions, and campaigns could be compared on them before an election rather than after a controversy.
The limits of the research matter. The conversations lasted a median of about 14 minutes, participants were paid, and the tested political issues were British. Real-world influence will depend on exposure, trust, repetition, and whether people willingly sustain political conversations with a machine. That uncertainty is a reason to test safeguards, not a reason to ignore the mechanism.
Democratic deliberation requires more than access to an answer. It requires time to understand, compare, and contest the answer. We do not need to ban machine speech to protect that space. We need to stop treating the machine’s ability to set the pace as if it were a neutral feature of the interface.
A democracy in which one side can generate an argument faster than the other side can read it is not necessarily better informed. It may simply be losing control of the clock.
Roney Lima do Nascimento is a mathematics educator, AI specialist and doctoral candidate in Pure Mathematics at the University of SĂŁo Paulo. He writes about model evaluation, education, democracy and institutional capacity. His work has appeared in Folha de S.Paulo, Nexo PolĂticas PĂşblicas, Congresso em Foco, EUobserver, GovInsider, HEPI and Educational Leadership.
U.S. President Donald Trump speaks briefly to the press as he arrives during the 81st United Nations General Assembly at the United Nations Headquarters in New York, on Sept. 22, 2026.
“To offend a strong man, tell him a lie. To offend a weak man, tell him the truth.”
That quote, often attributed to Marcus Aurelius, one of the great Stoics, perfectly encapsulates the very troubling and sad moment we’re in, and the very troubling and sad president we’re dealing with.
For all his bluster, Donald Trump is inarguably weak and small. His ego is easily bruised, his feelings are easily hurt. He’s been this way for a long time, and as Americans living in the Trump era, we’ve all come to see how deeply insecure, petty, and vengeful he can be.
His late-night social media rants lashing out at enemies both real and imagined; his impetuous and impolitic attacks on longtime allies who don’t roll over for him; his reckless primarying of his own party members who dare break from him, even if momentarily.
Hell, you could even say his very presidency is the result of an otherwise harmless roast by former President Barack Obama at the 2011 White House Correspondents Dinner that he took too personally.
But when things are going particularly badly for him, his skin is at its paper thinnest, and it shows.
Last week, Trump announced on social media that he was banning three news outlets — MS NOW, Politico, and CNN, where I work — from covering the White House. He hasn’t said why those particular outlets were targeted, giving the impression that this might not have been all that well thought out. Imagine that.
It does coincide, however, with Trump’s steadily sinking approval numbers, which just this week hit an abysmal record low, falling below 30% according to one poll.
Believe it or not, focusing on ballrooms and buildings, invading Greenland and Canada, suing political enemies and chasing crypto deals while we’re in an ill-advised war and most Americans are stretching to afford basic necessities, has not endeared him to voters.
It’s almost as if ignoring his key promises to voters — to lower inflation, to lower the cost of goods, to never get us into another war — has turned them off.
So, stuck in a bed of his own making with nowhere to go, he’s lashing out, this time at the press, simply for covering the bad decisions he’s made and their impact on voters.
But trying to punish or sideline the press will not change the fact that he and Republicans are in deep trouble. All it does is make him look brittle, desperate and weak.
It’s also a deeply disingenuous effort. Trump has justified his tantrum by saying the press is both “purposeful” and “fully coordinated” in reporting “fiction and lies.”
There’s a legal mechanism for the thing he’s talking about, and tellingly, he isn’t using it. Because, if true, what he’s describing is the textbook definition of actual malice, the legal bar used for defamation cases.
But he’s not suing the press, because this isn’t about the law, but rather his feelings. And it’s simply not our job to protect his feelings, or only publish news that makes him feel good. We’re not his parents or his kindergarten teachers.
He can throw all the fits he wants, and there’s no doubt his continual attacks on the press have had a chilling effect, but I know one thing: ultimately, the press will win this battle.
He may wish he were president of Russia or North Korea, where the free press doesn’t exist and state media tells only positive stories about Dear Leader, but here in America, the press is protected. Here, Americans want to live in a democracy, not a dictatorship. And here, we expect our political leaders to be able to take a metaphorical punch. Especially if the “punch” in question is simply the truth.
S.E. Cupp is the host of “S.E. Cupp Unfiltered” on CNN.
Influencer and left-wing political commentator Hasan Piker is introduced during a campaign rally for Wisconsin Gubernatorial candidate Francesca Hong on Aug. 2, 2026, in Milwaukee.
No one likes to think they’re being hysterical. And nothing makes hysterical people angrier than being described as such. So, let me offer my apologies in advance for what follows.
Jeremy Musighi, an independent researcher, recently released a study analyzing 14.7 million news articles published across 65 languages over the last decade. He found that, in 2024, news outlets published “more than twice as many stories about Israel as about all 48 countries of sub-Saharan Africa combined.”
Given that many countries focus on Israel to distract from their own problems, that’s not that shocking. But Musighi also found that, “On American cable news, Israel was the most-mentioned country— ahead of Iran, Russia, Ukraine, China. On the BBC’s news channel, 1 in every 17 broadcast minutes mentioned Israel.”
One might argue that given Israel’s recent actions, it earned such coverage. But Israel gets wildly disproportionate coverage in peacetime too. In 2019, long before the war in Gaza, “1 of every 112 news articles on Earth — from all the world’s outlets minus Israel’s — was primarily about Israel. 1.8 million international articles mentioned it.”
Now, I have reservations about Musighi’s methodology, given how media outlets recycle and syndicate content. But rather than wade into the statistics, for argument’s sake let’s just cut all of his findings in half. That would still be hard to defend rationally.
Also, this says nothing of the quality of the reporting. If it was objective, the quantity wouldn’t matter much. But a sizable portion is objectively anti-Israel, often taking Hamas’ claims at face value. When Hamas shrieked that Gaza was on the brink of famine, the Western press breathlessly repeated the claim, which was utterly false.
In a recent interview on Taqarrab, an Arab-language podcast, acclaimed photo “journalist” Motaz Azaiza admitted that he refused to publish images of Hamas abuses — to protect the abusers. The New York Times’ Nick Kristoff launched weeks of coverage with the claim that Israelis train dogs to rape Palestinians. American media covered the controversy over Macklemore’s criticisms of Israel like a free speech martyr.
In fairness, a lot of coverage is driven by news, like when the United Nations condemns Israel, which it has done 187 times since 2015 — nearly six times more often than it has condemned Russia, and 17 times more often than Iran or North Korea.
Founded in 1948, modern Israel is middle-aged as countries go.
Look at a list of 195 countries in order of their founding date and Israel ranks somewhere between the 85th- and 90th-oldest (if we acknowledge biblical Israel, then it’s the fourth-oldest nation in the world). And yet many talk about Israel as if it’s a sort of recent mistake, easily remedied, like outgoing mail that hasn’t been picked up yet.
Israel is only slightly older than modern Tibet, which was, according to the Chinese government, “liberated” in 1951. Many Tibetans use the term “occupied.”
We don’t hear much about “occupied Tibet” from people fixated on the evils of occupation. Nor do we hear about China’s system of apartheid— another charge casually hurled at Israel. Nor do we hear about China’s genocidal policies toward the Muslim minority Uyghur population in Xinjiang. The charge of cultural genocide is irrefutable, but there’s also strong evidence of more literal genocide thanks to the regime’s violent and coercive reproduction policies, slashing birthrates in half.
Nor do we hear much about China’s policy of settler-colonialism in Tibet and Xinjiang, where Han Chinese are incentivized to migrate as part of the regime’s policy of “sinicization.” I think China’s pretty powerful, but criticizing Israel, according to the popular online streamer Hasan Piker (who will not condemn China), is “speaking truth to power.”
To that end, we hear a lot about Israel’s occupation, settler-colonization and genocide. We don’t have the space to contend with all of these charges, so for the sake of brevity and reasonableness let’s concede, for now, the first two. Besides, it’s the false genocide charge that fuels anti-Israel obsessions more than any other. The claim is more than 40 years old. And yet, since the founding of Israel, the Palestinian population has grown more than fivefold.
When you hear that Israeli genocide proves Israelis are the “new Nazis” keep in mind that the Holocaust killed 2 out of every 3 European Jews.
A new documentary, “Naza,” that purports to prove that Israel is committing genocide bases its claim on the fact that Israel goes to great lengths to track collateral damage. Never mind that there’s no such thing as collateral damage when you intend genocide.
It should take the average reader five minutes to read this column. When “Naza” debuted at the Venice Film Festival this month, the audience’s standing ovation lasted five times as long. That, I’m sorry to tell you, is a sign of hysteria.
Jonah Goldberg is editor-in-chief of The Dispatch and the host of The Remnant podcast. His Twitter handle is @JonahDispatch.
New research shows AI beats elite debaters in persuasion — not through better arguments, but by out-pacing human reading and reply speed.
A political chatbot can answer before a citizen has finished reading its previous message. It can add statistics, historical examples, causal claims and links at a rate no human campaign volunteer could match. That does not necessarily make its case wiser. It changes the contest by allowing one speaker to control the tempo.
A June 2026 preprint offers unusually direct evidence of this problem. Across four preregistered experiments involving 18,978 conversations from 6,923 people, frontier AI systems were more persuasive than laypeople, tournament winners, professional canvassers and elite competitive debaters. The human experts were not casual opponents: the study included world and continental champions, paid preparation, live practice and substantial performance incentives.
The most revealing result came when the researchers changed the rules of the exchange. Elite debaters had produced replies averaging 54 words after roughly 95 seconds. The AI produced about 294 words with sub-second latency. When the system was constrained to roughly human-length messages and human writing speed, its advantage over coached elite debaters fell to a statistically indistinguishable zero.
The study is a preprint, not a final verdict, and its experiments were conducted in controlled text conversations in the United Kingdom. It does not prove that any chatbot will decide an election. It does, however, identify a democratic variable that is largely missing from public debate: persuasion throughput, or the amount of argumentative material a system can deliver within a citizen’s limited attention.
Our current safeguards focus on important questions. Was the message generated by a machine? Who paid for it? Was personal data used to target the recipient? Is the image or voice synthetic? Those questions address identity and targeting, but they do not address what happens after a person enters the conversation.
A chatbot can present dozens of claims before the user has inspected the first source. In the 2026 study, unconstrained AI produced about 37 fact-checkable claims per conversation; the human-paced version produced about 12. Across conditions, fact density strongly predicted persuasive impact. The advantage was not simply that the machine sounded more empathetic or more human. It could place more information on the table before its counterpart could answer.
A peer-reviewed Science article published in December 2025 points in the same direction from another angle. In experiments with 76,977 participants, 19 models and 707 political issues, post-training and prompting designed for persuasion mattered more than personalization or model scale. The same interventions that increased persuasion also reduced factual accuracy. Speed and density can therefore amplify useful information and error at the same time.
The appropriate response is not a government word limit on political speech. It is to make the tempo of machine persuasion visible and controllable. A political chatbot should display how many new factual claims it has introduced, how quickly it is producing them, and whether the user has asked for additional material. A citizen should be able to switch to a slow mode that presents one claim at a time, pauses until the evidence has been opened, and offers a neutral summary before moving on.
Sources should be attached to claims, not scattered as decorative links at the end of a long answer. The interface should separate established facts, contested interpretations, and predictions. It should also let the user request the strongest counterargument without forcing the person to leave the conversation and find an opposing system.
Political chatbots should identify the model version, the date of the governing instructions, and the organization responsible for the conversation. Campaigns, parties, advocacy groups, and platforms should preserve sample transcripts for independent audits. Those audits should measure claim accuracy, correction behavior, source diversity, and whether the system changes its standards when arguing for different sides.
These measures are viewpoint-neutral. They do not decide which position is correct or prevent a campaign from making a forceful case. They address a procedural asymmetry: one participant can generate arguments at machine speed while the other remains bound by human reading, memory, and verification.
Disclosure alone will not solve that asymmetry. A label saying that the speaker is a bot tells the citizen who is talking, but not how the interface is shaping the exchange. A clearly identified system can still overwhelm the user with a sequence of claims that arrives too quickly to compare, challenge, or remember. Transparency must therefore cover the mechanics of persuasion, not merely the identity of the persuader.
A useful standard could be tested without deciding which political arguments are permissible. Does the system count and display new factual claims? Can the user stop automatic continuation? Are sources attached to the precise claims they support? Can an independent reviewer reproduce a sample conversation under the recorded model and instructions? These are measurable design questions, and campaigns could be compared on them before an election rather than after a controversy.
The limits of the research matter. The conversations lasted a median of about 14 minutes, participants were paid, and the tested political issues were British. Real-world influence will depend on exposure, trust, repetition and whether people willingly sustain political conversations with a machine. That uncertainty is a reason to test safeguards, not a reason to ignore the mechanism.
Democratic deliberation requires more than access to an answer. It requires time to understand, compare and contest the answer. We do not need to ban machine speech to protect that space. We need to stop treating the machine’s ability to set the pace as if it were a neutral feature of the interface.
A democracy in which one side can generate an argument faster than the other side can read it is not necessarily better informed. It may simply be losing control of the clock.
Roney Lima do Nascimento is a mathematics educator, AI specialist and doctoral candidate in Pure Mathematics at the University of SĂŁo Paulo. He writes about model evaluation, education, democracy and institutional capacity. His work has appeared in Folha de S.Paulo, Nexo PolĂticas PĂşblicas, Congresso em Foco, EUobserver, GovInsider, HEPI and Educational Leadership.
President Donald Trump holds an image showing the size of Meta’s new data center during a cabinet meeting on Aug. 26, 2025.
I’ve covered a lot of elections — and most times, the issues that are going to really matter to voters aren’t brand new or sprung on them with just two months to go, arriving out of nowhere. They’re predictable — the economy, a war, a long-contentious policy fight.
This year is different. While most voters tell us their biggest concern is affordability, a new issue has entered the chat, taking up an outsized amount of space on the campaign trail, on the airwaves, and in party war rooms.
AI.
The advent of giant data centers, along with new fears about artificial intelligence and the companies overseeing the technology, as well as the growing issue of privacy and Flock cameras are all presenting a brand-new mine field for politicians and party leaders, and they are scrambling to adapt.
The issues are so new to voters, in fact, that neither Democrats nor Republicans seem to have a good handle on what their positions are.
In Pennsylvania, an important swing state with a number of key House races in play, Gov. Josh Shapiro did an about-face last month, seemingly acknowledging the growing opposition.
Just this past February, the Democratic potential presidential contender touted new data centers, promising it would help mitigate the risk that China would outpace us on AI.
But just a few months later, Shapiro signed an executive order dramatically reining in the tech, and calling out “predatory” developers.
Over on the right, Texas Gov. Greg Abbott performed the same jiujitsu, at first welcoming a $40 billion investment from Google last year, and then pausing approvals for about 1,800 data center projects this month.
Democrats seem to be settling into arguments over environmental and quality of life concerns, where Republicans are increasingly embracing arguments for more local control.
It’s undoubtedly a hasty response to a dramatic shift in public opinion this year, perhaps thanks to a high-profile case in Utah involving Kevin O’Leary from “Shark Tank,” in which public backlash forced him to drastically scale back his development plans from 40,000 acres down to roughly 20,000.
A new UMass Amherst poll shows 65% of Americans do not want data centers in their communities, and it’s the rare issue that crosses party lines.
Compounding fears was a terrifying warning from a former AI executive last week that AI could end civilization within a decade and the tech companies aren’t doing enough about it.
Rounding off the trifecta of worrisome tech issues are Flock cameras, automated license plate readers installed everywhere that capture vehicle and driver data.
None of this is popular with voters. But while both parties and candidates all over the country seem keenly aware of this fact, one political figure does not: Donald Trump.
In the past week, Trump has weighed into the controversies with what seems like reckless abandon in an election year.
He’s come out in favor of data centers, telling voters that otherwise they’ll be “backwards and poor.”
He’s called fears over AI “a hoax,” likening them to fears over climate change (also a hoax, to him), and rejected calls for more regulation.
And this week, he said he liked Flock cameras, despite jam-packed town halls full of angry citizens protesting the invasive technology.
Trump’s been unmoved by public opinion on other issues, of course, including the war in Iran, tariffs, abortion, and the Epstein files. And that may just see Republicans lose their majority this November. It’s never a good idea to tell voters they’re wrong about how they feel.
It’s also not often a brand-new issue threatens to upend an election cycle, and the president is telling worried voters it doesn’t matter.
Whether he cares or not, the people disagree.
S.E. Cupp is the host of “S.E. Cupp Unfiltered” on CNN.