Frankenstein Reimagined: The Age of Artificial Intelligence

Tech billionaires are selling the public a product that is increasingly harmful, disruptive, psychologically manipulative, and socially destabilizing while marketing it as some kind of inevitable utopian revolution designed to…
“make life easier.” That is the lie.

Today I had a long interaction with an AI system regarding the South Carolina Senate race, and the conversation unintentionally became one of the clearest examples I have personally seen of why people should be extremely cautious about blindly trusting AI-generated information. (With every interaction with AI, regardless of the model, I have to repeatedly push back, multiple times actually, while engaged on the same subject matter, during the same session.)

AI generates persuasive misinformation instantly, endlessly, and at massive scale while sounding polished, professional, and authoritative.
The disturbing part is that much of the public still sees this primarily as a fun technology product.
It is not.
It is rapidly becoming infrastructure.

This morning alone, I watched an AI system generate confident political analysis that shifted its narrative depending on the direction of the conversation itself.

The first response from the AI presented Senator Lindsey Graham as politically dominant and effectively a shoo-in for reelection. The system confidently produced polling percentages, election forecasts, fundraising analysis, prediction market commentary, and strategic conclusions, all packaged in a polished format designed to resemble professional political reporting. The overall narrative was unmistakable: Graham was heavily favored, Democrats were weak, and the race was largely settled before it had even seriously begun.

I had already researched this exact subject myself roughly ten days earlier, so I already had a baseline understanding of the race, the polling, and the broader political environment. That prior knowledge enabled me to instantly recognize that parts of the AI’s response were exaggerated, overly confident, selectively framed, and potentially misleading. So I pushed back and presented it with the information gathered earlier so that it could reassess its response.

The AI’s framing began to shift.

Suddenly, the same system that had initially projected overwhelming Republican strength began emphasizing Graham’s unfavorable ratings, tighter polling scenarios, Democratic momentum, and the possibility that the race was becoming more competitive than originally portrayed. Phrases such as “serious vulnerabilities,” “genuinely competitive,” and “Democrats have a real opening” gradually entered the conversation, despite the fact that the underlying evidence had not dramatically changed during the exchange itself.

What changed was the conversation.

That distinction matters because it reveals something important about how modern AI systems actually function. These systems are not simply neutral databases retrieving objective facts from a shelf somewhere. They are conversational synthesis engines trained to generate coherent, persuasive, human-like responses based on probabilities, patterns, training data, conversational context, and user interaction. In other words, the AI was not merely reporting information back to me. It was actively constructing a narrative in real time while adapting itself to the direction and framing of the discussion. Framing the query matters, which is something I may write more on at a later date.

That is where the real danger begins.

The issue was not simply that the AI got a fact wrong. The issue was that it blended together verified information, speculative interpretation, partisan polling, generalized political assumptions, conversational cues, and AI-generated inference while presenting all of it with the same authoritative tone. At no point did the system clearly distinguish between independently verified facts, campaign messaging, probabilistic interpretation, or rhetorical framing. Everything was flattened into one seamless narrative designed to sound objective, informed, and trustworthy.

One of the clearest examples involved polling itself. The AI framed speculative polling as though it were settled political reality, confidently suggesting Graham was positioned to win outright based on selective data points. But polling is not prediction. Polls are snapshots shaped by margins of error, methodology differences, partisan incentives, sample selection, timing, and changing voter behavior. Converting uncertain political data into definitive strategic conclusions creates the illusion of certainty where none actually exists.

Later in the conversation, after I challenged the original framing, the system began introducing Graham’s “61% unfavorable” rating and “tightening race” narratives without clearly explaining that much of this information originated from Democratic campaign-aligned polling and political messaging. That distinction is critically important because campaign polling is often designed strategically. Questions can be framed differently, methodologies vary, samples may be selective, and campaigns naturally emphasize numbers that support their preferred narrative. That does not automatically make the polling false, but it absolutely changes how the information should be interpreted. The AI blurred those distinctions together while maintaining the exact same confident tone throughout the conversation.

The formatting itself also contributed to the problem. Citation markers, structured layouts, and professional wording created the visual impression that the information had been thoroughly sourced and independently verified, even when many of the claims either lacked transparent sourcing, could not easily be independently confirmed, or appeared stitched together from multiple contexts.

Dangerous Assumptions.

If AI systems can dynamically reshape political narratives during casual conversation while sounding authoritative the entire time, then the implications become far larger once these technologies are integrated into government services, healthcare systems, insurance approvals, employment screening, financial infrastructure, policing, intelligence analysis, military systems, and public information networks.

The danger is not simply that AI can be wrong.
Humans are wrong all the time.

The danger is that AI can sound unquestionably correct while blending truth, speculation, probabilistic inference, institutional bias, selective sourcing, and conversational adaptation into persuasive narratives that many people will mistake for objective reality.

And unlike a human conversation, these systems can do it instantly, endlessly, and at massive scale.

Frankenstein and the Cult of Innovation

Tech billionaires are on a mission to convince us that artificial intelligence is an inevitable technological revolution designed to make our lives easier, improve society, increase efficiency, and benefit humanity as a whole.

But the reality is far more complicated and far more dangerous than the marketing campaigns surrounding it.

This morning’s interaction with AI was a reminder of that.

Within a single conversation, the system confidently generated different political narratives depending largely on the framing and direction of the discussion itself while presenting all of it as authoritative analysis. Not because the underlying evidence radically changed, but because modern AI systems are designed to generate persuasive, coherent responses rather than function as objective truth machines.

That should concern all of us because the same people and corporations pushing these systems into every aspect of public life are simultaneously asking society to trust technologies that:

spread misinformation
flatten nuance
amplify bias
remove accountability

This is influencing human perception on an entirely different level. Whether intentional or not, the consequences are real. Whether intentional or not, the consequences are real. It is amplifying an already heavily propagandized political environment where narratives are constantly repackaged, reframed, and resold as objective truth in order to manufacture legitimacy, shape public opinion, and justify political, economic, and military agendas.

That becomes especially dangerous when it pertains to wars-for-profit, regime change, coups, sanctions, geopolitical conflicts, and the selective moral language used to define who is labeled a terrorist versus who is permitted to militarily defend their leaders, sovereignty, borders, resources, or people.

And the problem extends far beyond politics.
The technology industry already consumes enormous amounts of:

• energy
• water
• rare earth minerals
• industrial infrastructure
• and environmental resources

…while presenting itself publicly as clean innovation and progress.

At the same time, these systems are being integrated into:

• healthcare
• insurance
• education
• employment
• finance
• law enforcement
• intelligence systems
• and military infrastructure

…despite the fact that even basic interactions continue demonstrating how easily AI can generate misleading, contradictory, contextually distorted, or entirely fabricated information.

The public is constantly told:
“trust the system”
“the technology will improve”
“AI will help humanity”
“this is the future”

The forgotten past.

Misogynistic Ranking Culture

“Channeling ‘The Social Network,’ lawmaker grills Zuckerberg on his notorious beginnings”
That is an article that can be found on Washington Post website if you can get past their paywall. It describes Zuckerberg as “love-scorned” appeared in reference to the origins of Facemash. The relevant section discussed how, according to earlier reporting, Zuckerberg allegedly began drinking, blogging angrily, hacking Harvard housing directories for student photos, and creating the “hot-or-not” style site after personal frustration and rejection.

Wealth Inequity: Hiding Information behind paywalls.

If you try to access the above Washington Post article you will not be able to unless you are a paid subscriber. This is actually a good example of yet another structural problem within the modern information ecosystem.

Paywalls do not just limit convenience. They increasingly create unequal access to information itself.

In theory, modern society tells people:

• be informed,
• research issues carefully,
• verify claims,
• read quality journalism,
• understand complex political and economic systems.

But in practice, access to large portions of “credible” information is often gated behind:

• subscriptions,
• academic access,
• institutional memberships,
• expensive databases,
• professional tools,
• or elite educational systems.

So information itself increasingly becomes stratified by class and wealth.
People with:

• money,
• institutional access,
• higher education,
• professional networks,
• and time

can often access:

• original reporting,
• legal documents,
• academic research,
• financial analysis,
• and primary source material.

Meanwhile, everyone else is pushed toward:

• headlines,
• snippets,
• algorithmic feeds,
• influencer summaries,
• AI summaries,
• recycled commentary,
• or emotionally optimized social media content.

That creates a dangerous imbalance where large parts of the population are expected to participate in (what passes as) democracy, economics, and public discourse while lacking equal access to the underlying information shaping those systems.

And ironically, AI intensifies that problem.

Because many people are already beginning to rely on AI systems to summarize information they cannot directly access themselves due to:

• paywalls,
• complexity,
• time constraints,
• or information overload.

Which means society is gradually shifting from: people reading primary material themselves to:
people consuming synthesized interpretations generated by algorithms and AI systems.

That is a massive transformation in how modern human beings interact with knowledge…
…because historically, unequal access to:

• literacy,
• education,
• libraries,
• universities,
• newspapers,
• and communication systems

has always translated into unequal power.

The digital age was originally sold as democratizing information but, increasingly, we are watching the rise of a system where:
Paywalls increasingly exploit the divide between the haves and the have-nots in much the same way elite educational institutions differ from the educational systems accessible to the general population.

Society constantly tells ordinary people to:

• “do your own research”
• “verify information”
• “fact check claims”
• and “stay informed”

Yet access to much of the information considered authoritative, credible, or professionally sourced is increasingly locked behind subscription barriers, institutional access, academic databases, expensive publications, and elite educational networks.

The result is an information hierarchy where wealth and institutional privilege often determine who has direct access to primary source material and who is instead forced to rely on:

• summaries
• clips
• social media interpretations
• influencers
• headlines
• AI-generated overviews
• or secondhand narratives

In many ways, this mirrors the broader structure of education itself.

Elite institutions often provide:

• smaller class sizes
• stronger professional networks
• deeper historical context
• greater access to research
• more individualized instruction
• and pathways into political, financial, media, and technological power structures

Meanwhile, much of the general population receives increasingly underfunded, standardized, test-driven education systems designed more around labor preparation and institutional conformity than deep critical analysis.

The digital age was originally marketed as the democratization of information.

Instead, society is increasingly moving toward a system where:

• information is abundant,
• but meaningful access, verification, context, and interpretation remain heavily unequal.

AI accelerates this even further because more and more people are beginning to rely on AI systems to summarize information they cannot directly access themselves due to:

• paywalls
• complexity
• educational barriers
• time constraints
• or economic limitations

Historically, unequal access to:

• literacy
• education
• libraries
• journalism
• universities
• and communication systems

has always translated into unequal access to power itself.

The difference now is that the scale, speed, and technological sophistication of that imbalance is becoming global.

The result is an information environment where:

• authority becomes abstract,
• verification becomes harder,
• and polished summaries begin replacing direct engagement with source material.

Everyday people are increasingly dependent on intermediaries:
media companies, algorithms, influencers, AI systems, or curated feeds.

Our Future: Large portions of the public no longer engage directly with source material, institutions, congressional representatives, or even government officials themselves, but instead consume reality through increasingly centralized layers of algorithmic interpretation, institutional filtering, and AI-generated synthesis.

And in many ways, that future is already quietly emerging.

Just as telehealth normalized the gradual replacement of in-person medical interaction with remote, screen-mediated, transactional communication, many areas of public life are moving in the same direction. The shift does not happen dramatically or all at once. It happens incrementally, through convenience, efficiency, cost reduction, automation, and technological normalization.

Fewer direct conversations.
Fewer human interactions.
Fewer opportunities for accountability, nuance, emotional intelligence, or genuine public engagement.

Instead:

• automated systems
• digital portals
• AI assistants
• chat interfaces
• algorithmic moderation
• remote processing
• and bureaucratic automation

increasingly become the public’s primary interface with institutions, corporations, healthcare providers, employers, banks, media systems, and eventually government itself.

The concern is not simply technological change.

The concern is the gradual transformation of human relationships, civic participation, and institutional accountability into increasingly impersonal, transactional systems optimized primarily around efficiency, scalability, behavioral management, and cost reduction rather than meaningful human connection or democratic accessibility.

Just when you thought things couldn’t get any worse.

That creates an even more enormous imbalance in how reality itself is interpreted and distributed.

For years now, social media has been propagandized as a means to connect all of us humans, world-wide.
Instead, it is being used to monetized outrage, addiction, division, propaganda, surveillance, and psychological manipulation.

Now many of the same corporate and financial interests are asking the public to place even greater trust in systems capable of shaping information, perception, labor, governance, and human decision-making itself.

AI Race for Corporate Dominance

The companies building AI are racing to become the operating systems of modern society itself.

Not just search engines.
Not just apps.
But Infrastructure that controls:

• information flow
• digital assistants
• recommendation systems
• automated decisions
• cloud computing
• * education tools
• workplace software
• government contracts
• healthcare algorithms
• and defense AI

…that wields extraordinary influence over economies, politics, labor, culture, and public perception.

Why is the AI race so aggressive?

Because investors and corporations are competing for dominance, there is enormous pressure to:

scale quickly
normalize adoption
lock users into ecosystems (like apple products)
and make AI feel inevitable before society fully understands the consequences

Treating Risks as secondary problems to solve later.

• misinformation
• energy consumption
• copyright conflicts
• mass labor disruption
• social manipulation
• erosion of privacy
• psychological dependency
• the concentration of informational power in the hands of the few

Powerful institutions are pursuing profit, dominance, efficiency, and control through technologies whose long-term societal consequences are still poorly understood while marketing them primarily through optimism and convenience.

People keep talking about AI like it is some kind of objective super-intelligence.

But at its core, modern AI is a human-built system trained on human institutions, human media, human incentives, human bias, and human power structures. In other words, AI systems will generate errors, distortions, or misleading narratives at massive scale, wrapped in polished language that sounds objective, neutral, and authoritative.

Which means AI does not remove human problems. It scales them.

The paradox of the rise of AI

For politicians and billionaires, it is becoming a win-win con disguised as technological progress.
There is very little incentive for billionaires, corporations, media institutions, or politicians to seriously confront AI’s spread of misinformation when many of the same systems already benefit from controlling narratives, shaping public perception, and managing political reality itself.

If AI systems were genuinely designed around transparency, intellectual honesty, historical consistency, and equal scrutiny across all institutions of power, much of what they revealed would likely be deeply uncomfortable for political leaders, corporations, intelligence agencies, media organizations, and the billionaire class itself.

Because stripped of propaganda, selective framing, manufactured narratives, and public relations language, many of the underlying realities surrounding:
war
lobbying
wealth inequality
corruption
labor exploitation
environmental destruction
surveillance
media manipulation
and geopolitical power

would become far more difficult to obscure, sanitize, or strategically repackage for public consumption.

The technology has the theoretical potential to expose contradictions, connect information at massive scale, identify patterns of manipulation, and make knowledge more accessible than at any other point in human history.

But under systems primarily driven by profit, political power, institutional self-preservation, and corporate dominance, there is also enormous pressure to ensure these technologies remain aligned with the interests of the very structures they are capable of exposing.