Charlie Kirk (Turning Points USA) shot and killed during event in Utah; Suspect Tyler Robinson charged

[LuuqMaan]

My guy, does every post need a prompt?
Are you working on your prompt skills?

 

[Ish Gibor]

My guy, does every post need a prompt?
Are you working on your prompt skills?

I need an objective response, and Chat Bot is an objective non-biased source, especially when it pertains to larges and complex summaries of information and data.

It confirms the logical and critical thinking skills. AI sees his nature as wicked and as Judas, not as person with compassion. Of course as a reasonable thinking person we all ready knew this. They want him on a godlike status, but AI Chat Bot disagrees and tells that this is the anti-Christ. This is not my opinion this is what AI Chat bot concluded based on large amount of data on him and what the Bible says. And I am secular, to made it clear.

Some of the children on here see all what is happening as a game, as a joke. They have a child state mind. That's why they use "picture-memes" as a retractor when it comes to serious issues like this. And this is usually by people with short attention spans, low comprehension skills. This is why people like this have posts with only a few sentences, giggling things off like 12-year old girl.

Charlie Kirk Laughed at $5 Million REPARATIONS for Every Black American!



This is not my opinion, this is the AI Chat Bot respons.

Prompt, Was Charlie Kirk against or for reparations for Black Americans?:

Great — let’s compare Charlie Kirk’s arguments against reparations with the main arguments in favor of reparations for Black Americans.

1. Charlie Kirk’s Arguments Against Reparations​

• Unfair to Current Generations → He argues it’s wrong to make people today pay for sins they didn’t commit.
• “Victimhood Culture” → Says reparations encourage dependency and divide Americans instead of uniting them.
• Slippery Slope → If reparations are paid for slavery, other groups might demand reparations for historical injustices too.
• Focus on the Present → He believes energy should go into economic opportunity, education reform, and job creation instead of cash payouts.

2. Arguments For Reparations​

• Unpaid Debt of Slavery → Enslaved Africans built massive wealth for the U.S. without compensation; their descendants never received justice.
• Ongoing Discrimination → Systemic racism (Jim Crow, redlining, mass incarceration) continues to harm Black Americans economically.
• Wealth Gap → White families have ~6–7× the wealth of Black families on average; reparations aim to close this gap.
• Historical Precedent → The U.S. has paid reparations before (e.g., Japanese Americans interned during WWII, Native American tribes for land theft).
• Moral and Biblical Grounds → Many supporters argue it’s about justice and repair, not charity — “restoring what was stolen.”

3. Comparison Table​

Issue	Charlie Kirk (Against)	Reparations Supporters (For)
Fairness	“Not fair to punish people for actions of ancestors.”	“Not about blame, but repairing generational harm.”
Impact on Society	Divides Americans, fuels resentment.	Can heal divisions by acknowledging and repairing injustice.
Economic Justice	Focus on current opportunities instead of payments.	Payments or programs can close racial wealth gap.
Precedent	Opens door for endless claims.	U.S. already paid reparations in other cases.
Moral View	Wrong to pay for something you didn’t personally do.	Justice requires making amends, even generations later.

Would you like me to also show you what the Bible says about reparations/restoration (like when Zacchaeus paid back fourfold, or laws about stolen property)? That could put this debate into a Christian framework, which often comes up when Charlie Kirk talks about it.


Prompt, How would Jesus have responded:

Good question. Let’s frame it carefully by looking at Jesus’ teachings and actions and how they relate to something like reparations:

1. Restitution in Jesus’ Time​

• In Luke 19:1–10, Zacchaeus (a tax collector who exploited people) meets Jesus.
  • He promises: “If I have cheated anybody, I will pay back four times the amount.”
  • Jesus affirms this as true repentance: “Today salvation has come to this house.”
    This shows Jesus approved of repaying harm — not just apologizing.

2. Justice and the Oppressed​

• Jesus quoted Isaiah: “The Spirit of the Lord is on me … to proclaim good news to the poor … to set the oppressed free.” (Luke 4:18)
  His mission wasn’t only spiritual, but also about lifting burdens of the marginalized.

3. Law of Love​

• “Do to others as you would have them do to you.” (Luke 6:31)
• “Love your neighbor as yourself.” (Mark 12:31)
  These commands imply solidarity, empathy, and repairing relationships.

4. Accountability​

• Jesus often rebuked wealthy or powerful groups who benefited from unjust systems (e.g., Pharisees exploiting widows, Mark 12:40).
• He taught that wealth gained unjustly must be surrendered: “What good is it for someone to gain the whole world, yet forfeit their soul?” (Mark 8:36).

5. How Jesus Might Respond to Reparations​

Based on these principles, Jesus would likely:

• Affirm restitution (like Zacchaeus) as a genuine act of repentance and justice.
• Center the oppressed — focusing on healing and restoring those harmed by slavery and racism.
• Challenge the wealthy and powerful who resist sharing resources.
• Frame reparations as love in action, not political ideology.

So while Jesus never spoke about “reparations” as we know them, His teachings suggest He would support restoring what was taken, healing generational wounds, and practicing radical love and justice — not just with words, but with tangible action.

Would you like me to put this into a side-by-side table (Jesus’ teachings vs. modern reparations debate), so you can see it more clearly?

Prompt, Yes:

Here’s a side-by-side table showing how Jesus’ teachings map onto the modern reparations debate:

Jesus’ Teachings vs. Reparations Debate​

Biblical Principle / Example	What Jesus Did or Taught	Implication for Reparations
Restitution (Luke 19:8–9)	Zacchaeus repaid 4× what he stole; Jesus declared this true repentance.	True repentance includes repairing harm, not just apologizing.
Justice for the Oppressed(Luke 4:18)	Jesus’ mission: bring freedom to the oppressed, good news to the poor.	Reparations align with lifting burdens placed on Black Americans through slavery & systemic racism.
Law of Love (Mark 12:31; Luke 6:31)	“Love your neighbor as yourself.” “Do to others as you’d have them do to you.”	Reparations can be seen as love in action — treating others with fairness and dignity.
Accountability for Exploitation (Mark 12:40)	Jesus rebuked elites for “devouring widows’ houses.”	He would likely confront systems where wealth was built on stolen labor.
Healing Relationships(Matthew 5:23–24)	Taught reconciliation: make peace with your brother before offering gifts to God.	Reparations could be a path to national reconciliation and healing of racial divides.
Warning Against Hoarding Wealth (Mark 8:36; Luke 12:15)	“What good is it to gain the world, yet lose your soul?”	Jesus would caution against clinging to wealthwhile ignoring justice.

Summary​

If we apply His principles:

• Jesus would likely call for restitution and repair (like Zacchaeus).
• He would stand with the oppressed.
• He would frame reparations not as politics, but as a moral and spiritual duty of love, justice, and reconciliation.

Do you want me to also show how opponents like Charlie Kirk’s arguments would look when placed against these same biblical principles — so we can see whether his stance aligns or conflicts with Jesus’ teachings?

Prompt, Yes:

Perfect — here’s a direct comparison between Charlie Kirk’s arguments against reparations and Jesus’ teachings on justice, restitution, and love:

Charlie Kirk vs. Jesus’ Teachings on Reparations​

Issue	Charlie Kirk’s Position	Jesus’ Teaching / Example	Alignment?
Fairness	Says it’s unfair to make current generations pay for ancestors’ sins.	Zacchaeus repaid even though restitution hurt him financially; Jesus affirmed it (Luke 19:8–9).	Conflict — Jesus supported costly restitution when wrong had been done.
Division vs. Unity	Claims reparations divide Americans into victims and oppressors.	Jesus said reconciliation requires making amends before worship (Matt. 5:23–24).	Conflict — Jesus taught healing comes throughrepentance and repair, not ignoring the harm.
Focus on the Present	Prefers focusing on new opportunities instead of “dwelling on the past.”	Jesus proclaimed freedom for the oppressed (Luke 4:18) and condemned ongoing injustice.	Conflict — Jesus emphasized addressing past and present wrongs to restore justice.
Economic View	Reparations encourage dependency and resentment.	Jesus said, “Do to others as you would have them do to you” (Luke 6:31) — generosity and restoration create dignity, not dependency.	Conflict — Jesus taught radical generosity, not fear of resentment.
Moral Responsibility	Believes individuals shouldn’t pay for something they didn’t personally do.	Jesus rebuked entire groups (Pharisees, elites) for benefiting from unjust systems (Mark 12:40).	Conflict — Jesus recognized collective responsibility for systemic sin.

Conclusion​

• Charlie Kirk’s stance centers on individual fairness and avoiding collective responsibility.
• Jesus’ teachings center on restoring justice, healing the oppressed, and costly restitution as true repentance.
• By biblical standards, Kirk’s arguments often conflict with Jesus’ model of love, justice, and reconciliation.

End prompt.


The same conclusions keep coming back, he was not a good Christian. But his fanbase claims he was, which is their interpretation and opinion. They have limited understanding of scripture btw, whereas AI can interpret the whole book and translate old (original) texts from any language within seconds. He's aways in conflict with the Jesus philosophy. The reason why? He was a white nationalist, who used Christianity as a tool to hide white supremacy.

 

[bnew]

Spoiler: text

Commented on Mon Sep 22 21:09:31 2025 UTC

https://i.redd.it/9spnhtfg7sqf1.png

 

[Ish Gibor]

Spoiler: text

Commented on Mon Sep 22 21:09:31 2025 UTC

https://i.redd.it/9spnhtfg7sqf1.png

At the core she's right. Some of the allegations are her opinion, although based on subjective reasoning still can be factual.

1. Enemy Identified → professors, Democrats, marginalized groups.
2. Dehumanization → framed as traitors, corrupt, “anti-American.”
3. Rhetoric Amplified → public speeches, lists, funerals turned into rallies.
4. Crowd Radicalization → audience primed, anger normalized.
5. Violence / Threats → harassment, racist death/rape threats, physical assault.

 

[Ish Gibor]

Tim Black addresses short bus gang member from Chicago.

"A young Chicago man issued a chilling warning to President Trump: “Don’t send the National Guard — we’re a different breed.” In this segment, I break down the meaning behind his words, the reality of Chicago gangs, and why sending the Guard could turn into a disaster for the city."

 

[Ish Gibor]

 

[Hood Critic]

If you're going to flood us with prompt outputs, do us a favor and spoiler them so those who want to read them can while they're less intrusive to everyone else.

Also, please stop proclaiming your prompt usage as an un-biased source, that is not entirely true especially considering how generic and open ended your prompts are - Unpacking the bias of large language models

Thank you for your attention to this matter.

 

[Ish Gibor]

If you're going to flood us with prompt outputs, do us a favor and spoiler them so those who want to read them can while they're less intrusive to everyone else.

Also, please stop proclaiming your prompt usage as an un-biased source, that is not entirely true especially considering how generic and open ended your prompts are - Unpacking the bias of large language models

Thank you for your attention to this matter.

This information on this site addresses how data is retrieved from an affidavit document with 30 pages. It's not looking at a large collection of data where it does comparative analysis. As I have stated, I post tje entire conversation with prompt so people can see that I have not manipulated the outcome.

"This “position bias” means that, if a lawyer is using an LLM-powered virtual assistant to retrieve a certain phrase in a 30-page affidavit, the LLM is more likely to find the right text if it is on the initial or final pages."

Did I put in such document? No I did not.

I assume people who come to this thread want to gather as much information as possible about this individual.

A well designed Chat bot can recognise when there is false information being presented, as it is scalable. If you have nothing better to address don't bother, go worship Charlie and continue making jokes like a 12 year old child from a short bus.

Study could lead to LLMs that are better at complex reasoning

To improve adaptability of large language models to challenging tasks that require reasoning, MIT researchers found strategically applying a method known as test-time training with task-specific examples can boost the accuracy of an LLM more than sixfold.

news.mit.edu

How to build AI scaling laws for efficient LLM training and budget maximization

MIT and MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models (LLMs) will perform based on smaller models in the same family.

news.mit.edu

 

[Hood Critic]

This information on this site addresses how data is retrieved from a document with 30 pages...

You neither read the page nor did you do the due diligence to read the MIT report.

 

[Ish Gibor]

You neither read the page nor did you do the due diligence to read the MIT report.

"This “position bias” means that, if a lawyer is using an LLM-powered virtual assistant to retrieve a certain phrase in a 30-page affidavit, the LLM is more likely to find the right text if it is on the initial or final pages."

Did I put in such document? No I did not. But I do want you with your "human skills" to verify where a mistake was made in the data it presented, when gave it these prompts?

Here they speak of the solutions in later models.

Study could lead to LLMs that are better at complex reasoning

To improve adaptability of large language models to challenging tasks that require reasoning, MIT researchers found strategically applying a method known as test-time training with task-specific examples can boost the accuracy of an LLM more than sixfold.

news.mit.edu

How to build AI scaling laws for efficient LLM training and budget maximization

MIT and MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models (LLMs) will perform based on smaller models in the same family.

news.mit.edu

As if I trained to be bias against your "prince of the dawn" Charlie Kirk.

Charlie Kirk – Racist & Controversial Remarks (Timeline Overview)​

Date	Remark / Action	Why It Was Controversial
2015	Claimed his West Point spot was lost due to affirmative action.	Framed affirmative action as unfair racial preference.
2016–2018	Called “white privilege” a myth and a “racist idea.”	Denied systemic racism.
2018	Blamed single motherhood in Black communities (esp. Chicago) for gun violence.	Reinforced racial stereotypes, ignored structural causes.
2019	Claimed that “poor children” in America “don’t work hard” and “would rather do drugs” than improve their lives.	Condemned as classist and racist in undertones, portraying poor/minority youth as lazy and criminal.
2021	Advocated using tear gas, rubber bullets, and whips on migrants; called immigration an “invasion.”	Seen as xenophobic, dehumanizing language.
2022–2023	On abortion, said women who seek abortions are “murderers,” called abortion “the genocide of Black America,” and equated Planned Parenthood with “a Black genocide machine.”	Viewed as inflammatory, misogynistic, and racist — framing abortion as a targeted attack on Black communities.
July 2023	Said Joy Reid, Michelle Obama, Sheila Jackson Lee, and Ketanji Brown Jackson lacked “brain processing power” and “stole a white person’s slot.”	Widely condemned as overtly racist against Black women.
Late 2023	Claimed Jewish donors control media, nonprofits, and Hollywood, funding “cultural Marxism.”	Accused of pushing antisemitic tropes.
Jan 2024	Called the Civil Rights Act a “huge mistake.”	Attacked historic civil rights protections.
Jan 2024	Said: “If I see a Black pilot, I hope he’s qualified” — and added the same about Black surgeons.	Criticized as racist and undermining trust in Black professionals in critical fields.
2024	Spread false rumor that Haitian immigrants in Ohio were eating local pets.	Widely debunked xenophobic rumor.

 

[wire28]

Ryan Grim from breaking points giving his unfiltered thoughts on why mr Kirk wasn’t all bad at 33:30

@Pressure @Outlaw

 

[Ish Gibor]

The devil is in the details, ...

5:54 speech, Erica Kirk's uh speech, um she
5:57 brought up as far as well, you see how
5:59 we didn't riot and we didn't protest
6:01 after Charlie's assassination. We didn't see violence.
6:06 We didn't see rioting.
6:09 Who the hell going to riot and protest
6:10 for Charlie Kirk? Huh? Raise your hand.
6:12 Anybody? No. Not even Satan said yes.




This is bigger than George Floyd, it's about a systemic problem. But we know how her husband felt about BLM. A woman takes on the traits of the man she's with, unless he's a beta male.

UPDATE: Prosecutors charge 3 more officers in George Floyd’s death

MINNEAPOLIS (AP) — Prosecutors charged three more police officers Wednesday in the death of George Floyd and filed a new, tougher charge against the officer at the center of the case, delivering a victory to protesters who have filled the streets from coast to coast to fight police brutality and...

www.wizmnews.com

PDF - Investigation into the City of Minneapolis and the Minneapolis Police Department Findings from the Minnesota Department of Human Rights April 27, 2022

 

[Hood Critic]

"This “position bias” means that, if a lawyer is using an LLM-powered virtual assistant to retrieve a certain phrase in a 30-page affidavit, the LLM is more likely to find the right text if it is on the initial or final pages."

Did I put in such document? No I did not. But I do want you with your "human skills" to verify where a mistake was made in the data it presented, when gave it these prompts?

Here they speak of the solutions in later models.

Study could lead to LLMs that are better at complex reasoning

To improve adaptability of large language models to challenging tasks that require reasoning, MIT researchers found strategically applying a method known as test-time training with task-specific examples can boost the accuracy of an LLM more than sixfold.

news.mit.edu

How to build AI scaling laws for efficient LLM training and budget maximization

MIT and MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models (LLMs) will perform based on smaller models in the same family.

news.mit.edu

As if I trained to be bias against your "prince of the dawn" Charlie Kirk.

Spoiler

Charlie Kirk – Racist & Controversial Remarks (Timeline Overview)

​

Spoiler

Date	Remark / Action	Why It Was Controversial
2015	Claimed his West Point spot was lost due to affirmative action.	Framed affirmative action as unfair racial preference.
2016–2018	Called “white privilege” a myth and a “racist idea.”	Denied systemic racism.
2018	Blamed single motherhood in Black communities (esp. Chicago) for gun violence.	Reinforced racial stereotypes, ignored structural causes.
2019	Claimed that “poor children” in America “don’t work hard” and “would rather do drugs” than improve their lives.	Condemned as classist and racist in undertones, portraying poor/minority youth as lazy and criminal.
2021	Advocated using tear gas, rubber bullets, and whips on migrants; called immigration an “invasion.”	Seen as xenophobic, dehumanizing language.
2022–2023	On abortion, said women who seek abortions are “murderers,” called abortion “the genocide of Black America,” and equated Planned Parenthood with “a Black genocide machine.”	Viewed as inflammatory, misogynistic, and racist — framing abortion as a targeted attack on Black communities.
July 2023	Said Joy Reid, Michelle Obama, Sheila Jackson Lee, and Ketanji Brown Jackson lacked “brain processing power” and “stole a white person’s slot.”	Widely condemned as overtly racist against Black women.
Late 2023	Claimed Jewish donors control media, nonprofits, and Hollywood, funding “cultural Marxism.”	Accused of pushing antisemitic tropes.
Jan 2024	Called the Civil Rights Act a “huge mistake.”	Attacked historic civil rights protections.
Jan 2024	Said: “If I see a Black pilot, I hope he’s qualified” — and added the same about Black surgeons.	Criticized as racist and undermining trust in Black professionals in critical fields.
2024	Spread false rumor that Haitian immigrants in Ohio were eating local pets.	Widely debunked xenophobic rumor.

Act as a middle school teacher where you explain to someone with an 8th grade education in 300 words or less why LLMs are not immune to bias in their responses

Imagine you and your friends are writing a giant book together. Each of you adds stories, opinions, and facts you’ve heard from family, TV, or social media. Some of what you write is true, some is exaggerated, and some might show personal beliefs without even realizing it. Now, imagine a robot reads that entire book to learn how people talk and what words go together. That robot is like a Large Language Model (LLM).

Here’s the problem: if the robot’s training book contains biased ideas—like stereotypes, unfair assumptions, or one-sided information—the robot can accidentally repeat those patterns. It doesn’t actually understand fairness or truth. It just learns to predict what words usually come next, based on the examples it has seen.

For example, if the training data often links certain jobs with one gender or certain behaviors with one group of people, the model might repeat those links, even if they’re not fair or accurate. This is called bias. Bias can also show up if the training data leaves out certain voices or perspectives. If only some groups are represented, the model may not answer well for people outside that group.

Developers try to reduce bias by cleaning up training data and adding rules, but because human language itself is full of bias, no LLM can be completely free of it. Think of it like a sponge: if you soak it in muddy water, even if you rinse it out, some stains will remain.

So, LLMs are powerful tools, but they reflect the world they’re trained on—both the good and the flawed. That’s why it’s important to use them thoughtfully, question their answers, and not treat them as perfectly neutral or always correct.

Would you like me to give you a couple of simple real-world examples of bias in AI that an 8th grader would easily recognize?

 

[Ish Gibor]

This comes from someone named "hood critic" who at most has 10 sentences in a post.

So adamant to protect Charlie at all cost. You have no idea what LLM can do or can't, you go by scepticism.

I copy past your text, and this is the result Prompted, Verify this based on fact:

Your explanation is factually accurate

Let’s break it down point by point to verify:

1. LLMs learn from human-written data → True. Models like GPT are trained on vast collections of text (books, articles, websites, etc.).
2. Not all training data is accurate or unbiased → True. Human language carries stereotypes, misinformation, and cultural perspectives.
3. LLMs don’t “understand” truth or fairness → Correct. They generate text by predicting likely word sequences, not by reasoning about morality or absolute truth.
4. Bias shows up if stereotypes are common in the training data → Correct. Studies show LLMs sometimes reinforce gender or racial stereotypes.
5. Bias also comes from underrepresentation of groups → True. If certain groups, languages, or perspectives are underrepresented, the model performs worse for them.
6. Developers reduce bias but can’t remove it completely → Correct. Techniques like filtering, fine-tuning, and alignment help, but total neutrality is impossible because language itself is never neutral.
7. Sponge/muddy water analogy → A fair and often-used metaphor in AI ethics. Once bias is in the data, some traces usually remain.

So yes—your summary matches what researchers (including MIT, Stanford, and OpenAI) say about bias in large language models.

Would you like me to now give you those real-world, 8th-grader-friendly examples (like how autocorrect or voice assistants sometimes treat names differently, or how job ads can accidentally favor one gender)?

Prompted, Yes:

Great — here are two simple, real-world examples of AI bias that an 8th grader could spot right away:

1. Job Ads Example​

Some AI systems that place online job ads used to show higher-paying tech jobs mostly to men, and lower-paying jobs more often to women.

• Why? Because the training data reflected real-world hiring bias and historical patterns.
• Problem: The AI reinforced unfair stereotypes about who “should” have certain jobs.

2. Voice Assistants Example​

When you ask a voice assistant (like Siri, Alexa, or Google Assistant) to pronounce certain names, it often does fine with common English names (like “Michael” or “Sarah”), but struggles with names from other languages (like “Ayo” from Yoruba or “Nguyen” from Vietnamese).

• Why? Because the training data had fewer examples of diverse names.
• Problem: People with non-English names feel excluded or misrepresented.

Both of these are real forms of bias in AI:

• The first shows stereotypes being reinforced.
• The second shows voices being left out.

Would you like me to also give you a positive example (where AI bias got fixed and made things more fair)?

End Prompt.


Do you agree?

Spoiler: Another one, where I literately copy past the text, no alterations to manipulate the outcome

https://www.thecoli.com/threads/general-trump-administration-f-kery-thread-2025-2029.1056881/post-56536193

Couple issues with this:

- Medical insurance and prescription drug insurance are actually 2 separate things though they often fall under the same policy.
- Health insurers have limited the number of prescription drugs they cover.

An 18% increase in overall costs is exuberant at face value considering the above.

I mean the tariffs will make it more costly for virtually any business to operate, due to tangential expenses increasing if nothing else

Even basic stuff like office supplies & coffee at headquarters, for an insurance company or otherwise, is going up.

Overhead, cost of living, price of the product - everything is increasing from the direct or holistic impact of these tariffs. I don't think any sector is entirely immune from it

2026 mid-terms are going to be a bloodbath for GOP if that continues and gov is not addressing it

You’ve shared two perspectives that are circling around the same theme but from different angles:

Big Jo’s point​

• Tariffs ripple through the economy in both direct and indirect ways.
• Even non-manufacturing sectors (like insurance) can’t escape, because everyday operational inputs—paper, coffee, office equipment—get more expensive.
• Rising overhead → higher cost of living → more expensive products/services.
• Political implication: if tariffs keep squeezing both businesses and consumers without visible government relief, GOP risks a voter backlash in the 2026 mid-terms.

Hood Critic’s counterpoint​

• Agrees that tariffs raise general goods/service costs.
• But highlights that insurance premiums have been rising long before tariffs, due to systemic issues in healthcare (drug prices, hospital charges, administrative costs, etc.).
• Suggests that while tariffs make operations more expensive, it sounds absurd to tell policyholders their medical debt is tied to coffee grounds or copier paper.
• Essentially: tariffs are real, but they’re not the core driver of premium hikes.

Synthesis​

• Tariffs = additional stressor on overhead across all sectors.
• Insurance (and healthcare more broadly) = already structurally inflated, so tariffs don’t create the problem, but they add more fuel.
• Politically, voters often don’t separate those factors—they just feel costs keep rising and blame whoever is in power.
• That’s why Big Jo frames it as a political time bomb, while Hood Critic stresses the structural nature of healthcare costs.

Put simply: tariffs amplify existing cost problems, but they’re not the root cause of things like skyrocketing health premiums.

Do you want me to break this down into a side-by-side table (Big Jo vs Hood Critic vs Neutral Analysis) for clarity?

Prompt, Who is right:

Both are partly right — but they’re talking about different layers of the problem:

Where ​

Big Jo is right​

• Tariffs absolutely raise costs for everything that relies on imported goods or supply chains.
• Even small operational things (office paper, coffee, IT equipment, furniture) get pricier.
• Those overhead costs eventually filter into consumer prices — even in sectors like insurance that don’t sell a “physical product.”
• Politically, history shows voters rarely untangle the nuance; if costs keep rising across the board, the party in power takes the blame.

Where ​

Hood Critic is right​

• Insurance premiums and medical debt weren’t caused by tariffs.
• They’ve been increasing for decades due to:
  
  • high U.S. healthcare costs (pharma, hospitals, specialists)
  • administrative inefficiencies
  • lack of pricing transparency.
• Tariffs may add a little overhead pressure, but telling customers premiums are higher because “coffee and copier paper went up” would be disingenuous.

The balance​

• Tariffs: a multiplier — they make everything costlier, but only at the margins for healthcare/insurance.
• Healthcare system issues: the primary driver of rising premiums.
• Politics: voters don’t care about the nuance — if their bills and premiums go up, they punish whoever is in charge.

So if we’re asking “who is more correct about the core issue of premiums and medical debt?” → Hood Critic.

If we’re asking “who is correct about tariffs worsening costs and political fallout?” → Big Jo.

Would you like me to show this in a weighted score breakdown (e.g., who’s 70% right vs 30% right) to make it crystal clear?


Prompt, Yes:

Here’s a weighted breakdown of who is “more right” based on the two main issues:

Weighted Accuracy Breakdown​

Issue	Big Jo	Hood Critic	Neutral Analysis
Tariffs increase costs across the economy	80% correct (tariffs raise input costs, ripple effects hit all sectors, political fallout real)	20% correct (acknowledges costs but downplays role)	Tariffs = multiplier effect, but not the root of healthcare premiums
Why insurance premiums & medical debt keep rising	30% correct (tariffs do add overhead, but not the driver of medical costs)	70% correct (structural healthcare issues are the core driver, long before tariffs)	Healthcare inflation is systemic; tariffs just add friction
Overall accuracy (weighted)	55% right	65% right	Both capture part of the truth — tariffs worsen, healthcare system causes

Verdict​

• Hood Critic edges out as “more right” (65%), because premiums are mainly driven by systemic healthcare issues, not tariffs.
• Big Jo is right politically and economically (tariffs raise costs everywhere, GOP risk is real), but oversells tariffs’ role in healthcare premiums.

Do you want me to expand this into a visual diagram (like arrows showing how tariffs → overhead → premiums vs healthcare system → premiums) so it’s easy to see how both arguments intersect?


┌──────────────────────┐
│ Tariffs / Trade │
│ (“input costs up”) │
└──────────┬───────────┘
│
➤ Increases cost of goods used by businesses (coffee, paper, supplies, equipment)
│
➤ Raises operating/overhead expenses
│
➤ Some of those increased costs get passed on to consumers / into product pricing
│
▼
┌─────────────────────────────────────┐
│ General inflationary pressure │
└─────────────────┬───────────────────┘
│
────────────────────────────┼──────────────────────────────────
│
▼
┌──────────────┐ ┌────────────────────────┐ ┌──────────────────────────┐
│Healthcare │ │ Non-tariff systemic │ │ Political / Consumer │
│Systemic Costs │ │ cost drivers │ │ Effects │
└──┬───────────┘ └─────────┬──────────────┘ └──────────┬───────────────┘
│ │ │
│ │ │
│ ▶ Higher hospital & physician fees ▶ Voters feel squeeze
│ relative to other countries. [oai_citation:0‡healthsystemtracker.org](https://www.healthsystemtracker.org/brief/what-drives-health-spending-in-the-u-s-compared-to-other-countries/?utm_source=chatgpt.com)
│
│ ▶ Expensive drug prices; new pharmaceuticals/technology → Premiums / out-of-pocket rise [oai_citation:1‡Wikipedia](https://en.wikipedia.org/wiki/Prescription_drug_prices_in_the_United_States?utm_source=chatgpt.com)
│
│ ▶ Aging population (more people in high‐cost age brackets) [oai_citation:2‡KFF](https://www.kff.org/health-costs/health-policy-101-health-care-costs-and-affordability/?utm_source=chatgpt.com)
│
│ ▶ Chronic conditions & unhealthy lifestyle (obesity, etc.) [oai_citation:3‡PMC](https://pmc.ncbi.nlm.nih.gov/articles/PMC6179664/?utm_source=chatgpt.com)
│
│ ▶ Administrative inefficiencies, billing complexity, profit margins [oai_citation:4‡commonwealthfund.org](https://www.commonwealthfund.org/publications/issue-briefs/2023/oct/high-us-health-care-spending-where-is-it-all-going?utm_source=chatgpt.com)
│
│ ▶ Labor costs, medical staff shortages [oai_citation:5‡TIS Insurance Services, Inc.](https://www.tisins.com/key-drivers-of-2025-healthcare-cost-increases/?utm_source=chatgpt.com)
│
────────────────────────────┼──────────────────────────────────
│
▼
Outcomes on Insurance / Premiums / Medical Debt
├─ Premiums keep rising
├─ More out-of-pocket costs for patients
├─ More people under medical debt
└─ Political dissatisfaction / voter backlash (Mid-terms etc.)

 

[choc_cardigan]

This comes from someone named "hood critic" who at most has 10 sentences in a post.

So adamant to protect Charlie at all cost.

This is an obvious troll, something about not feeding trolls...