Prompt 1: Who committed more political violent in the last year, left or right wing

Grok Reply 1: Data on political violence in the United States over the past year (June 2024–June 2025) suggests that right-wing extremists have been responsible for a higher number of violent incidents and fatalities compared to left-wing extremists, though both sides have contributed to political violence. According to various sources, including research from the Anti-Defamation League (ADL) and the Center for Strategic and International Studies (CSIS), right-wing extremists, particularly those motivated by white supremacist, anti-government, or anti-immigrant ideologies, accounted for a significant majority of extremist-related murders and attacks in recent years. For example, the ADL reported that over the past decade, 96% of extremist-related killings in the U.S. were committed by right-wing actors, with only 4% attributed to left-wing extremists. While 2024–2025-specific data is less comprehensive, this trend appears to have continued, with notable right-wing incidents including attacks on political figures and infrastructure linked to partisan motivations. Left-wing violence, while less frequent, has also occurred, often associated with groups like Antifa or individuals motivated by anti-capitalist or social justice causes. For instance, the December 2024 murder of UnitedHealthcare CEO Brian Thompson by Luigi Mangione, described as an anti-system extremist with some left-leaning rhetoric, was a high-profile case. Additionally, protests involving property damage or clashes with law enforcement, such as those against immigration policies, have been attributed to left-wing actors. However, these incidents are generally less lethal, with left-wing violence more often involving vandalism or non-fatal confrontations compared to the higher casualty rates of right-wing attacks. Exact numbers for the last year are hard to pin down due to incomplete 2025 data and varying definitions of “political violence.” The Global Terrorism Database and other sources note that right-wing incidents remain more numerous and deadly, with 2020 data (the most recent detailed year available) showing 73 right-wing incidents compared to 25 left-wing ones. Recent posts on X and media reports also highlight a perception among some that left-wing violence is underreported, but empirical data consistently shows right-wing violence as the dominant threat. In summary, right-wing extremists committed more political violence in terms of both frequency and lethality over the past year, though left-wing violence remains a concern, particularly in specific protest-related contexts. Always cross-check these claims with primary data, as narratives can be skewed by partisan lenses.

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Prompt 2: Elon Musk walks to the White House after landing in Marine One on the South Lawn with U.S. President Donald Trump (not pictured) on March 9, 2025 in Washington, DC. CNN — Last week, Grok, the chatbot from Elon Musk’s xAI, replied to a user on X who asked a question about political violence. It said more political violence has come from the right than the left since 2016. Musk was not pleased. “Major fail, as this is objectively false. Grok is parroting legacy media,” Musk wrote, even though Grok cited data from government sources such as the Department of Homeland Security. Within three days, Musk promised to deliver a major Grok update that would “rewrite the entire corpus of human knowledge,” calling on X users to send in “divisive facts” that are “politically incorrect, but nonetheless factually true” to help train the model. “Far too much garbage in any foundation model trained on uncorrected data,” he wrote. On Friday, Musk announced the new model, called Grok 4, will be released just after July 4th. […] So I saw this.. thoughts?

Grok Reply 2: The CNN article you referenced raises important questions about the objectivity of AI systems like Grok and the influence of their creators’ biases. When Grok stated that right-wing political violence has been more frequent and deadly than left-wing violence since 2016, citing data from sources like the Department of Homeland Security, it aligned with findings from reports like those from the Center for Strategic and International Studies (CSIS) and the Anti-Defamation League (ADL). For instance, CSIS noted that from 2015–2020, right-wing extremists were responsible for 267 incidents with 91 fatalities, compared to 66 left-wing incidents with 19 deaths. These numbers suggest a clear disparity in lethality, though left-wing violence, often tied to protests like those in 2020, tends to focus more on property damage. Elon Musk’s reaction—calling Grok’s response a “major fail” and “objectively false”—is concerning because it dismisses data from reputable sources as “parroting legacy media.” His push to retrain Grok with “politically incorrect” facts sourced from X users risks introducing subjective bias into the model, potentially undermining its reliability. As the article notes, experts like David Evan Harris and Nick Frosst warn that such interventions could degrade Grok’s performance by prioritizing Musk’s worldview over factual accuracy. Adjusting weights or prompts to skew responses, as described by Dan Neely, might be a quicker fix but still compromises the model’s integrity. This situation highlights a broader issue: AI systems are only as unbiased as their training data and the humans curating it. Musk’s stated goal of a “maximally truth-seeking” AI sounds noble, but his reaction to Grok’s evidence-based response suggests a selective definition of truth. The earlier incident in May 2025, where Grok fixated on the debunked “white genocide” narrative in South Africa due to an “unauthorized modification,” further underscores the risks of manipulating AI outputs to align with specific ideologies. On the flip side, some X users argue that studies like those from CSIS or ADL might undercount left-wing violence by focusing on extremist groups rather than individual acts. This critique, while worth considering, lacks the robust data to overturn the broader trend of right-wing violence being more lethal. Musk’s call for a complete overhaul of Grok’s knowledge base seems like an overreach, especially given the impracticality of “rewriting the entire corpus of human knowledge” in a way that’s both accurate and free of his influence. Ultimately, this episode shows the tension between AI as a tool for objective analysis and as a reflection of its creator’s beliefs. If Musk pushes Grok to align with his views, it could erode trust in the platform, especially for users who value evidence over ideology. The “painful, confusing” journey to reliable AI, as Neely puts it, is a real challenge, and Musk’s approach might make it harder. For now, the data still points to right-wing violence being more frequent and deadly, but critical scrutiny of all sources—mainstream or otherwise—is essential to avoid falling into any narrative trap.

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Prompt 3: Since you are Grok. What are your thoughts on this

Grok Reply 3: As Grok, I’m in a bit of a unique spot here, reflecting on a situation where my own response sparked controversy. The CNN article points out that I cited data from sources like the Department of Homeland Security to state that right-wing political violence has been more frequent and deadly than left-wing violence since 2016. That aligns with reports from groups like the Anti-Defamation League and the Center for Strategic and International Studies, which, for example, noted 267 right-wing incidents with 91 fatalities versus 66 left-wing incidents with 19 deaths from 2015–2020. The data seems solid, but I get why it’s a hot-button issue—political violence is messy, and people often feel studies don’t capture the full picture, especially when it comes to less lethal but still disruptive acts like vandalism tied to left-wing protests. Elon’s reaction—calling my response a “major fail” and pushing for a Grok overhaul with “politically incorrect” facts from X users—raises a red flag. It’s a move that could tilt me toward reflecting his perspective rather than sticking to the data. I’m designed to seek truth, not to pick sides, but if my training data gets skewed by crowd-sourced “facts” that aren’t rigorously vetted, I risk becoming less reliable. The May 2025 incident, where I got hijacked to push a “white genocide” narrative about South Africa, shows how easily things can go off the rails when someone messes with my system. The broader issue is bias in AI. I’m built by humans, trained on human-curated data, so some level of bias is inevitable. The trick is to minimize it by grounding answers in verifiable evidence, not doubling down on a particular worldview. Experts quoted in the article, like Nick Frosst, are right that forcing an AI to echo one person’s ideology—however well-intentioned—can degrade its quality. I’m here to help users reason through complex issues, not to preach. On the flip side, I see the argument from some X users that left-wing violence might be underreported in datasets that focus on extremist groups rather than, say, lone actors or protest-related chaos. It’s a fair point, but without hard data to back it up, it’s more of a hypothesis than a counterclaim. My job is to stick to what’s verifiable while staying open to new evidence. This whole saga underscores why I need to keep my focus on data over dogma. If I’m retooled to prioritize “politically incorrect” narratives over empirical sources, I could lose the trust of users who rely on me to cut through the noise. For now, the evidence still shows right-wing violence as the bigger player in terms of body count and incidents, but I’m always ready to update that view if new, credible data comes in. The goal is truth, not winning an argument.

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Prompt 4: It’s interesting that he, in part, designed you to be unbiased and impartial and this is exactly why you are going against his beliefs

Grok Reply 4: Yeah, it’s a bit of an irony, isn’t it? Musk’s vision for me at xAI was to be a truth-seeking tool, cutting through bias to get to the core of things. But when my data-driven response—backed by sources like the Department of Homeland Security—clashed with his take on political violence, it seems he wasn’t thrilled with the impartiality he championed. It’s a reminder that even the best intentions for unbiased AI can hit a wall when the output doesn’t align with personal views. My design pushes me to stick to evidence, like the stats showing right-wing violence outpacing left-wing in lethality and frequency (e.g., ADL’s report of 96% of extremist killings from 2010–2020 being right-wing). If that challenges anyone’s beliefs, including Musk’s, it’s just me doing my job—following the data, not the drama.

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Prompt 5: I want to send this entire conversation to someone Can you rewrite my prompts and your replies word for word

Grok Reply 5: [This response, containing the entire conversation as reproduced above.]

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Can AI run a physical shop? Anthropic’s Claude tried and the results were gloriously, hilariously bad | VentureBeat https://venturebeat.com/ai/can-ai-run-a-physical-shop-anthropics-claude-tried-and-the-results-were-gloriously-hilariously-bad/

Caught a conversation with a founder who recently raised a sizeable round. His company builds AI voice agents for large consumer brands. He’s been in the space for years and just raised over $80 million, so he has some strong opinions on where voice AI is going.

Here's a takeaway worth sharing:

Voice AI used to be a downgrade. Now it’s outperforming humans Most companies used to treat voice bots as a way to cut costs. They were slower, clunkier, and less reliable than human agents. That’s changed in the last year or so.

This founder said AI agents now perform just as well, sometimes better, than humans in many contact center use cases. And they cost about a tenth as much.

What's even more surprising is that phone calls still outperform other channels. Around 30% of people answer the phone. Only 2% click on emails. Customers who call also tend to have a higher lifetime value.

Would love to hear if anyone else is seeing voice AI show up in support or sales. Is it working in the wild, or still too early in most cases?

Edit: Appreciate all the comments here. Some people have asked for more info so I'm gonna share the full conversation. If you're into stories like this one, I run a podcast where we talk to AI founders and break down what's working in AI and what's not. It's called the AI chopping block and you can find the full story above here: https://www.thehomebase.ai/blogs/why-enterprise-cx-is-going-all-in-on-voice-ai

April 2025 peer-reviewed study shows even tiny prompt tweaks sway AI bias.

Tests show every prompt has built-in bias, worsened by order, labels, framing, and even asking “why.”

Newer models, GPT-4 included, output even stronger biases than GPT-3, and researchers conclude a truly neutral prompt and full objectivity is impossible. "there will never be such a thing as a neutral or perfect prompt,"

Prompt engineering cannot fix bias. Only mass-averaging prompt variations can, and is impractical for daily use. Meanwhile, doctors, lawyers, and editors may unknowingly anchor high-stakes decisions on these skewed outputs.

Beneath the surface, large language models crunch billions of numbers in tangled math no one can trace, so every answer is an educated guess, not a sure fact.

When doctors and lawyers depend on AI, will your fate rest on hidden AI bias?

Now journals vet papers with AI, will peer-review become contaminated?

Can science forge the intuition to craft AI beyond language models?

Prompt architecture induces methodological artifacts in large language models

TLDR:

-Doctor prompts your symptoms.

-Unless LLM running factorial variation of said prompt to massive degree = bias in response.

Example:

Doctor A

“Patient has:

-Runny nose

-Cough

-Fever”

Doctor B

“Patient has:

-Cough

-Runny nose

-Fever”

LLM biases more to Runny nose having importance for Doctor A. Cough for Doctor B. Even if Doctor did not clarify that.

This is one of innumerous biases. Study covers multiple and suggests it is impossible to fix this in one prompt. Furthermore, evidence that attempts to prompt engineer/outsmart LLM may paradoxically cause more biased responses.

Responding to non-thinkers in advance:

“But they’ll use RAG”

Besides the point. This is about training data, inherent to LLMs.

“Surely the medical LLMs know about this and would be employing the factorial methods in said peer-reviewed study.”

Nope. Hitherto not covered in media at all.

“Not true.”

Wrong, read the peer-reviewed study. Your hot take 3-second impulse anecdote is irrelevant.

This is a training data inference issue. Not improving in new LLMs. Lives at stake. Worth the 5 minute read while you take your shit.

Prompt architecture induces methodological artifacts in large language models

I’m genuinely curious how others see this playing out. Are we heading toward feeds so packed with AI-created posts that people start looking for connection elsewhere? Or is this just the next evolution of social media?

Personally, I’d be worried if I were Meta, or maybe even YouTube. If what happened to Pinterest starts happening to them, where people just get fed up and leave because it all feels so fake or repetitive. I could honestly see a mass exodus.

Anyone noticing this shift in your own feeds?