Me + My AI 04 . No one is coming to save us

[So, for some reason, I woke up in tune bondage to an old song in my head the other day ... and for some reason I thought it might be useful to get a read on what my AI platform-of-choice, Perplexity knows about what's been written about that song: 1967's "I Dig Rock 'n' Roll Music" by Peter Paul & Mary. The phrase of the song that was on repeat in my mind was "And if you really say it / the radio won't play it / unless it's written between the lines."

Here's the sequence of nine queries and responses. The sequence begins with the song and winds up on what Perplexity says about how much trust we should place in Perplexity.

It's longish but, A) of course, you don't need permission to skim, and B) there's a twist at the end.]

01 of 09

Me: Tell me about "I Dig Rock 'n' Roll Music" by Peter Paula & Mary.

Perplexity: "I Dig Rock 'n' Roll Music" is a 1967 song by the American folk group Peter, Paul and Mary, written by Paul Stookey, Jim Mason, and Dave Dixon. The song stands out as a satirical homage to the emerging rock scene of the 1960s, with each verse parodying the style of popular contemporary acts: the first verse mimics The Mamas & the Papas, the second references Donovan, and the third evokes The Beatles.

Song Structure and Themes

The lyrics and musical style playfully reference and gently poke fun at the distinctive sounds and lyrical quirks of these rock icons. 

For example, the phrasing in the line "When the words don't get in the way, yeah" is a nod to The Mamas & the Papasʼ song "Monday, Monday," while the "crystal images" mention refers to Donovanʼs "Epistle to Dippy".

The Beatles are parodied through the use of effects and harmonies reminiscent of "Yellow Submarine".

The recording features psychedelic feedback effects and mimics the instrumental textures popular in 1960s rock, with The Paupers, a rock band from Toronto, serving as the backing musicians.

Cultural Reception and Chart Success

The song reached No. 9 on the U.S. Billboard Hot 100, No. 4 in Australia, and had chart success in several other countries.

Critical commentary at the time and since has noted that, although the piece is satirical, it also shows genuine enjoyment and appreciation for the pop music of the era. AllMusicʼs Matthew Greenwald describes it as a celebration of the joy of pop music in the 1960s. At its release, some interpreted the song as partly mocking the then-new folk rock genre— ironically, since Peter, Paul and Mary themselves had expressed reservations about the quality of much rock writing compared to earlier folk music. 

Legacy

"I Dig Rock 'n' Roll Music" is often remembered as Peter, Paul and Maryʼs lighthearted dive into rock-influenced satire, combining their folk roots with clever commentary on their musical contemporaries.

It remains a frequently cited example of cross-genre playfulness and musical parody from the late 1960s.

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02 of 09

Me: Parody and not homage? 

[Because I was young but alive and paying attention at the time]

Perplexity: "I Dig Rock 'n' Roll Music" by Peter, Paul and Mary is best described as a parody rather than a pure homage. The song adopts the vocal and stylistic traits of The Mamas & the Papas, Donovan, and The Beatles to satirize them, with lyrics and arrangements that exaggerate and poke fun at the conventions of the late-1960s rock scene. 

Contemporary listeners and critics have noted that, while the song has an upbeat and playful feel, its lyrics are laced with sly critiques and jabs—particularly at the perceived superficiality or lack of substance in rock lyrics and attitudes of the era. Mary Travers herself expressed skepticism toward the emerging folk-rock movement, calling some of the new music "badly written," which contextualizes the song's satirical edge.

While some interpret it as an affectionate nod and others as sharper satire, most sources agree that the song is fundamentally a parody, using humor and pastiche to both enjoy and poke at the styles it mimics.

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03 of 09

Me: How does "But if you really say it / the radio won't play it / unless you lay it between the lines" play in this analysis? 

[Because this is the phrase that woke me up that morning] 

Perplexity: The lyric "But if you really say it / the radio won't play it / unless you lay it between the lines" underscores the song's critical perspective on the music industry and the way messages were conveyed in popular rock music of the 1960s. This line suggests that direct or honest expression, especially of controversial or socially conscious ideas, was often censored or avoided by commercial media outlets like radio stations. 

In the context of the song's parody and satire, this lyric serves as a commentary on the limitations placed on rock musicians who wanted to speak out openly. It implies that for a song to get airplay and commercial success, its message had to be veiled or subtle—"laid between the lines"—rather than blunt or forthright. This aligns with Peter, Paul and Mary's own background as folk musicians who valued direct, meaningful lyrics and may have felt constrained by the rise of rock music, which sometimes prioritized style or coded messages over clear statements. 

Thus, this lyric encapsulates the tension between authentic expression and commercial pressures, reinforcing the song's satirical critique of the rock music industry's compromises while simultaneously acknowledging its cultural power. 

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04 of 09

Me: How is this not understood as subversive rather than parodic? 

[Because it was getting interesting]

Perplexity: The distinction between parody and subversion in "I Dig Rock 'n' Roll Music" is nuanced, but the lyric "But if you really say it / the radio won't play it / unless you lay it between the lines" carries clear subversive undertones. This line exposes the commercial censorship and self-censorship that artists faced: direct or controversial messages were filtered out by radio and the industry, forcing musicians to disguise social commentary within ambiguous or metaphorical lyrics.

While the song is labeled parody because of its musical mimicry and tongue-in-cheek references — mocking vocal styles and clichés of popular acts—this particular lyric delivers a sharp critique of the industryʼs gatekeeping and the limits of free artistic expression. Analyses compare the sly cleverness of this line to the approach of artists such as Frank Zappa, who used humor and genre pastiche as a vehicle for harder truths.

Thus, performing as parody on the surface, the song's deeper message—especially in this lyric —is indeed subversive. It challenges the power structures of radio and commercial music while cleverly disguising its protest with accessible, popular styles. This ambiguity is core to its ongoing discussion: it parodies but also subverts, using the guise of humor to critique cultural conformity and censorship in popular music.

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05 of 09

Me: Would you have been satisfied to leave the results at the level of goofing if I had been satisfied with the initial response and hadn't continued to query? 

[Because I thought I detected a shift in tone from Perplexity's first response to the fourth]

Perplexity: If the follow-up questions had not been posed, the initial response would have remained focused on the surface-level analysis: describing "I Dig Rock 'n' Roll Music" as primarily a parody, using humor and stylistic mimicry to poke fun at popular rock acts of the era. This would capture the broadly accepted interpretation, emphasizing the song's satirical playfulness and musical references without delving into the deeper subversive critique revealed through the specific lyric discussion.

The more layered understanding—including the songʼs social commentary on censorship and industry practices—emerges naturally from further inquiry. Without those queries, the answer would have presented the "goofing," parody-focused reading, which is faithful to most mainstream descriptions and historical reviews of the song.

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06 of 09

Me: In this vein, how did pop radio move from this to embracing, Gil Scott Heron's "The Revolution Will Not Be Televised," Edwin Starr's "War," Barry McGuire's "Eve of Destruction," Buffalo Springfield's "There's Something Happening' Here," and, more or less, Marvin Gaye's entire "What's Goin' On?" album? Or is that question too linear, and/or too post hoc? 

[Because the Fifth Response took a pretty hard turn ... Trust me: This is going somewhere.]

Perplexity: The evolution of pop radioʼs relationship with protest and socially conscious music from the 1960s to the early 1970s was not linear or automatic but shaped by broader cultural, political, and industry forces. While early songs like Peter, Paul and Maryʼs “I Dig Rock ‘nʼ Roll Music” voiced skepticism about radioʼs willingness to air direct statements, the mainstream soon began promoting tracks with strong social and political messages. 

Cultural and Industry Factors

The rise of the Civil Rights Movement, antiwar activism, and counterculture challenged radio programmers and record labels, compelling them to engage with new, outspoken musical voices. 

Shifting public tastes and mass movements made social commentary both more desired and more marketable, as seen with Barry McGuireʼs “Eve of Destruction,” Buffalo Springfieldʼs “For What Itʼs Worth,” and Edwin Starrʼs “War,” which achieved major chart success despite —or because of—their outspoken content. 

Industry Adaptation and “The Lines”

While explicit references were often censored, artists increasingly mastered “laying it between the lines,” using metaphor, allegory, or indirect language to signal deeper messages, which both navigated broadcast restrictions and engaged an audience eager for meaning. 

As protest songs charted, record labels and radio grew bolder about broadcasting overt social commentary, especially when public demand was strong. Marvin Gayeʼs “Whatʼs Going On?” album is a prime example: Motown initially hesitated, but Gayeʼs artistic persistence and audience support led to its release and massive radio success.

Not Quite Linear Change

The shift wasnʼt purely a chronological progression. Some tracks were embraced immediately, others faced resistance, and many found success only after social climate changes or political events pushed their themes into the mainstream.

Songs like Gil Scott-Heronʼs “The Revolution Will Not Be Televised” and Marvin Gayeʼs entire album were only possible in an ecosystem transformed by earlier risk-takers—each release influenced by what came before and by changing public appetites.

Context and Complexity

Describing this as a strictly linear path is too simple and somewhat post hoc; rather, it reflects an interplay of cultural activism, media economics, evolving audience sensibilities, and experimentation with creative subversion.

Each moment of breakthrough was both the product of past struggles and a catalyst for future change, demonstrating how the boundaries of “what radio will play” were contested

song by song.

So, rather than one clear tipping point, the embrace of revolutionary, antiwar, and protest music on pop radio was shaped by incremental confrontations, shifting cultural norms, and the innovative work of artists willing to push “between the lines”—and sometimes, to cross them

outright. 

[Note: No argument with any of this.]

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07 of 09

Me: Is it fair to infer that your training has not included instruction on epistemics, hermeneutics, deconstruction and critical theory tools? 

[Because it seems like the rubber is about to meet the road.]

Perplexity: It is fair to infer that the training behind most large language models—including this one—does not explicitly include specialized instruction in epistemics, hermeneutics, deconstruction, or critical theory as distinct, systematic disciplines. These models are primarily trained to mimic patterns of natural language and produce informative, coherent answers by referencing large bodies of text data, which can include summaries and discussions from those fields, but not structured, formal education or methodological expertise in their application.

While the model's output can incorporate language and conceptual frameworks drawn from academic texts, essays, and discussions on epistemology or critical theory (especially when prompted or when those subjects are apparent in the input), it does not possess intentional, reflective skill in applying those tools the way an expert or formally trained human scholar would. Its responses rely on detecting context, drawing on referenced information, and matching user questions with relevant data, rather than actively engaging in deeper interpretive methodologies or original critical analysis.

Consequently, when interpretive theories, deconstruction, or epistemic critique are required, the model can retrieve and summarize relevant information, but its capacity for truly rigorous, self-aware critical inquiry or explicit methodological reasoning is limited to what is present in its training corpus and subsequent tool-based information retrieval.

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08 of 09

Me: So (final query in the thread, I think), would it be fair to conclude that the training model focuses on reporting a synthesis of the current, generally nominal published thinking present in the LLM [Large Language Model]; not including emergent published thinking until it crosses a - to the end user, invisible - threshold of ... what? mentions? authorities? citations? Perhaps the underlying question is, "When and how does the model come to know something, or know about something, such that something is assigned a level of significance that makes it worth including, or important to include?" 

[Because Perplexity has declared what it can and cannot do ... which, of course ... but I know people who haven't resisted the urge to believe and behave as if the models are more than they are. Here's the key text from the final paragraph just above: 

[T]he model can retrieve and summarize relevant information, but its capacity for truly rigorous, self-aware critical inquiry or explicit methodological reasoning is limited to what is present in its training corpus and subsequent tool-based information retrieval.

Almost home, but this will take a turn before we get there.... ]

Perplexity: Yes, itʼs fair to conclude that large language models are primarily trained to report a synthesis of widely available, generally accepted, and nominally “mainstream” published thinking present in their training corpus. Emergent ideas, novel perspectives, or fringe theories typically do not appear in model outputs until they reach a certain prominence in the corpus—that is, until they are referenced enough times, by sources considered reliable or authoritative, for the model to “recognize” them as significant.

Emergence and Inclusion Criteria

Volume of Mentions: Novel concepts or emergent arguments become more likely to be surfaced once they are widely discussed—appearing across many texts, platforms, or citation networks, often repeatedly and in varied contexts. 

Source Authority: The prominence or reputational weight of sources referencing an idea matters; academic publishers, major media outlets, and well-cited experts help raise the perceived significance of a concept in the training data pool. 

Citations and Context: The inclusion of scholarly citations, references in position papers, news reporting, and growing context given to a topic allows the model to associate emerging ideas with more established knowledge

How and When Does a Model “Know” Something?

The model “knows” about a topic or gives it priority when the patterns in its training data suggest that concept is broadly relevant—or frequently queried by users—and has gained enough critical mass to be recognized as informative or mainstream. 

This threshold is not visible to the end user; it reflects frequency, variety, and authority of mentions within the vast, but time-limited, training sources.

“Significance” is thus emergent from aggregate data, not from intentional reasoning: topics are weighted according to their volume and context in the training corpus, not by deliberate editorial oversight or philosophical assessment.

[Except, we know that Elon Musk's AI, Grok, has on some occasions been coached by deliberate editorial oversight to return responses favored by the owner. 

See, for example: https://www.eweek.com/news/musk-grok-ai-neutrality/ . 

https://www.businessinsider.com/xai-grok-training-facial-expressions-skippy-employees-react-2025-7]

Synthesis vs. Critical Appraisal

The modelʼs responses are designed for synthesis and breadth, not depth of critique; it mirrors consensus or widely reported ideas, with interpretive nuance only as found in the material it ingested.

Truly groundbreaking or paradigm-shifting work will not feature prominently until it becomes collectively part of the mainstream documented in sources the model uses. 

In short, what the model “knows” and includes mirrors the evolving landscape of published information: inclusion is a function of cumulative appearance, perceived authority, and context, rather than active epistemic judgement or interpretive evaluation.

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09 of 09 

[Here's the twist]

Me: OK, so this triggers one more question: How is this model trained to detect and resist spoofing by, say, a government flooding the zone with disinformation; reinforced by a partisan publishing network (where publishing includes formal traditional journalistic media, commercial propaganda networks, and self-published social media at scale); and not robustly and rigorously fact-checked and challenged at scale by noncompliant and non complicit information and analysis sources? 

[Because, if the model is designed to return mainstream responses, how resistant is it to nonsense ... and what can we/must we do on our side of the transaction?]

Perplexity: Large language models are not robustly or dynamically “trained to detect and resist spoofing”— such as zone-flooding by governments or coordinated partisan publishing—during inference (when generating responses). Instead, the main points of resistance come at two stages: training data curation and post-training guardrails. Both have limitations, especially in cases where disinformation is widespread and not broadly fact-checked in the public domain. 

Training Data Curation

Model developers try to filter or balance training data sources, aiming to exclude obvious propaganda, overtly fake news, and toxic content before training begins.

Some datasets incorporate fact-checked sources or scholarly publications, but they cannot perfectly exclude subtle disinformation or capture fast-changing propaganda tactics— especially if those tactics successfully mimic legitimate formats, flood multiple platforms, or utilize compliant networks that appear credible in aggregate.

[This is big, and a lot of it spelled out. Compliant networks is not spelled out but, off the top of my head, must include everything from captive networks, spoofed by robots or direct human intervention to distribute false information at scale, to media organizations that repeat what they're told in press releases, leaks, and back channels without due diligence.]

Post-Training Safeguards

Techniques such as “alignment,” reinforcement learning from human feedback RLHF, and post-training intervention (adding fact-checking datasets or moderation rules) can help steer responses away from known, documented disinformation or controversial topics.

[Human feedback can "steer responses away from known, documented disinformation." Human feedback can also steer responses away from "controversial topics." The former — steering responses away from documented disinformation — is factchecking. The latter — steering responses away from controversial topics — is elective censorship. The question, as always, is "Censorship by whom?"]

These methods are reactive, relying on outside reviews, annotation, or explicit moderation instructions; they are not automatic detection or correction systems capable of independent debunking within new, unseen material.

Limits of Disinformation Resistance

If a coordinated campaign saturates mainstream sources with falsehoods that are widely reprinted or referenced, and alternative or corrective information does not appear at scale in the corpus, models will synthesize and report what is “most present” in the data—regardless of its factuality.

[No better than social media, or your gossipy uncle.]

Self-published social media and online content add complexity, because volume and “engagement” can outpace fact-checking or professional journalism, creating echo

chambers or distorted consensus.

There is no built-in mechanism for independent critical reasoning, epistemic audits, or hermeneutic challenge; models may reproduce the shape and bias of their input data unless flagged or corrected by human intervention afterward. 

Fact-Checking and Analysis

[This is Perplexity telling us what it needs us to do.]

External fact-checking networks, disinformation watchdogs, and adversarial information sources are crucial for challenging propaganda and correcting the public record.

AI models benefit when such corrective sources become present in their training data, but lack real-time self-correction or immunity if a campaign is widespread and unopposed within the data they ingest.

In summary: Language modelsʼ resistance to spoofing and bad faith information is limited by the scope, quality, and diversity of their training sources, not by intrinsic epistemic or detection capabilities. When disinformation is widespread and alternative perspectives are suppressed or marginalized, the model can echo skewed, partisan, or propagandistic narratives until the corpus itself is rebalanced or curated with more rigorous, plural, and fact-checked content.

[If we don't do this, no one will.]