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Persuasive Essay Sample: Should There Be Stricter Ethics for AI-Generated Vertical Video?

UUnknown

2026-02-17

10 min read

Annotated persuasive essay sample arguing for stricter ethics for AI-generated episodic vertical video—research notes, sources, and policy asks for 2026.

Hook: Why this matters to students, teachers, and creators in 2026

Deadlines, unclear sources, and the rise of AI tools mean you can write faster—but also risk making serious ethical mistakes. If you're drafting a persuasive essay about platform policy, AI ethics, or mobile-first media, you need a model that shows strong argumentation, up-to-date evidence, and transparent research practices. This annotated sample argues for stricter ethics for AI-generated episodic vertical video—a timely policy argument in 2026 as companies like Holywater scale AI-powered vertical streaming and regulators respond to deepfake controversies.

Quick take (inverted pyramid)

Main claim: Policymakers, platforms, and creators should adopt stricter ethics rules for AI-created episodic vertical video to protect consent, prevent deepfakes, preserve discoverability fairness, and ensure accountability.

Most important evidence: 2025–2026 incidents of nonconsensual sexualized AI content and platform-driven discoverability shifts; major vertical-video investments (Holywater’s January 2026 funding) that scale risk; ongoing regulatory action such as California investigations into AI-facilitated abuses and the EU AI Act implementation phase.

Actionable policy asks: mandatory provenance metadata, age- and consent-verification protocols, platform liability carve-outs tied to transparency, and funding for creator compensation and audits.

Annotated persuasive essay sample (For stricter ethics)

Introductory paragraph

Every week in 2026, millions of viewers choose short serialized stories on their phones—mobile-first platforms like Holywater are turning episodic vertical video into a mainstream entertainment format. As these platforms scale with AI tools that generate scripts, synthetic actors, and algorithmically optimized story arcs, a parallel surge in harms has followed: nonconsensual sexualized deepfakes, undisclosed synthetic performers, and manipulative attention-design that targets minors and vulnerable viewers. Because episodic vertical video combines immediacy, intimacy, and monetization, policymakers must enact stricter ethics standards for AI-created vertical content to protect consent, transparency, and equitable discoverability.

Annotation: The intro frames urgency (audience pain point: new tech + harms) and names a specific company and year to show currency and expertise. It ends with a clear thesis that sets up a policy argument.

First, consent is the core ethical failure in many AI-generated harms. Recent incidents, including investigations into AI chatbots and deepfake image production, show how easy it is to sexualize real people without permission. In January 2026 California's attorney general opened inquiries into AI-driven nonconsensual sexually explicit content and platforms scrambling to respond. Episodic vertical video makes things worse because serialized short formats normalize repeated exposure—once a synthetic or nonconsensual image appears in a series, it spreads across recommendations and social reposts. Stricter rules should require platforms to implement robust provenance metadata on AI-generated assets and to adopt verified consent records before monetizing or distributing realistic synthetic performers.

Annotation: Uses recent regulatory moves to support the claim. "Provenance metadata" is an actionable solution—students should cite sources that discuss technical feasibility and legal precedent.

Body paragraph 2 — Transparency and discoverability

Second, transparency is necessary for fair discoverability. In 2026, search behavior is shaped by social feeds and AI summarizers as much as traditional search engines. When platforms hide whether a clip is synthetic or generated by an AI process, recommendation systems can unintentionally amplify misleading content. A platform like Holywater, which raised new funding in January 2026 to expand AI-driven vertical streaming, will likely rely heavily on algorithmic promotion. Without disclosure requirements, synthetic episodic content can capture attention and ad revenue while drowning out human creators and misinforming viewers. Policies should mandate clear labeling and algorithmic explainability for AI-generated episodes, and require platforms to publish impact audits showing how their recommendation systems treat synthetic vs. human-created content. Model language for audits and disclosure can draw on audit-trail and compliance playbooks such as audit-trail best practices.

Annotation: Connects discoverability trends (2026) with platform economics and offers concrete policy instruments: labeling and audits. The Holywater funding detail situates the argument in current industry scaling.

Body paragraph 3 — Labor, compensation, and cultural harm

Third, ethics must consider labor and cultural impacts. AI-generated episodic series may replace writers, actors, and editors, disproportionately affecting early-career creators who depend on short-form platforms for discovery. Without rules for synthetic content, platforms will reap returns while creators lose opportunities and credit. Ethical standards should include compensation frameworks for creators whose styles or likenesses inspire synthetic characters, copyright protections for datasets, and mechanisms for collective bargaining. At minimum, platforms should disclose whether episodic storylines were generated using datasets trained on identifiable creators and pay residuals when those creators’ work materially influences synthetic output. For creator-oriented business and compensation patterns, see approaches discussed in creator commerce and live-drop playbooks.

Annotation: Broadens argument beyond safety to economic justice—useful for policy audiences. Suggests concrete remedies (residuals, disclosure) which are researchable and defensible.

Counterargument and rebuttal

Opponents will argue that strict rules stifle innovation and burden startups, especially mobile-first firms trying to scale novel formats. But regulation can be calibrated: lightweight provenance standards and phased compliance schedules protect small developers while requiring high-risk actors to meet stronger obligations. In addition, transparency and consent do not preclude innovation—they create a healthier marketplace where human and synthetic creators coexist with trust. Policymakers in the EU and several U.S. states have already shown how targeted rules can curb harms without halting investment. Practical compliance patterns for small platforms and edge deployments are described in engineering and compliance guides like serverless edge for compliance-first workloads.

Annotation: Acknowledge and rebut objections—essential for persuasive essays. Point to regulatory precedents in 2024–2026 to show feasibility.

Conclusion and policy asks

As vertical AI-driven episodic video platforms scale, ethical failures will scale too unless we act. Policymakers, platforms like Holywater, and creators should adopt a three-part framework: (1) mandatory provenance and consent metadata, (2) transparency and algorithmic impact audits, and (3) compensation and dataset accountability for creators. These measures safeguard viewers, preserve fair discoverability, and support creative economies—protecting what makes serialized vertical video culturally valuable in 2026 and beyond.

Annotation: The conclusion reiterates the thesis and leaves the reader with precise policy recommendations—good practice for persuasive, policy-oriented writing.

Source notes — where the evidence comes from

Industry scaling and funding: Holywater's January 16, 2026 funding round reporting describes rapid scale of AI vertical video (Forbes coverage). Use this to show market incentives and scale risks. For platform tooling and product predictions see StreamLive Pro — 2026 predictions.
Regulatory action: January 2026 investigation headlines about AI chatbots and nonconsensual imagery (California AG probe into xAI/Grok) highlight real-world harms that informed policy urgency.
Discoverability trends: 2025–2026 analyses of discoverability and social search show how audiences find content across social, search, and AI answers; cite digital PR and social search discussions for algorithmic influence.
Legal frameworks: The EU AI Act implementation phase (2024–2026) and recent state-level actions offer model provisions and demonstrate enforceability timelines.

Research tips and practical steps (for students and teachers)

Use these research strategies when you adapt this sample into an essay, policy memo, or classroom assignment.

1. Find recent, authoritative sources

Search news databases (ProQuest, Nexis) and industry outlets (Forbes, TechCrunch) for terms like "Holywater funding 2026" or "AI vertical video" to capture investment and industry context.
Use Google Scholar for peer-reviewed studies on deepfakes, consent, and media effects. Query with filters set to 2023–2026 for the latest scholarship.
Consult government press releases and agency reports for legal actions (e.g., state AG press pages) to ground claims in official investigations.

2. Document technical claims with primary sources

For technical feasibility (provenance metadata, watermarking), cite standards bodies (W3C, IEEE) or technical preprints that test watermark robustness. Engineering and SDK patterns for on-device and edge tooling are summarized in object-storage and edge guides like object storage reviews and companion app templates from CES reporting (CES companion app templates).
Use platform blog posts and engineering notes to understand recommendation algorithms and monetization models—these are primary sources for industry practices.

3. Cite correctly and avoid plagiarism

When you quote or paraphrase, include inline citations and a reference list in MLA or APA. Example (MLA): Fink, Charlie. "Holywater Raises Additional $22 Million." Forbes, 16 Jan. 2026.
Keep a research log with URLs, access dates, and short notes so you can reconstruct your evidence trail if a grader or editor asks.
Use quotation marks for verbatim text and paraphrase with your own framing. Run your draft through a plagiarism checker if your institution requires it.

4. Collect qualitative data quickly

Interview creators on platforms like TikTok or with vertical-series experience; use short structured questions about discoverability and compensation. Compact creator field kits and capture workflows are discussed in practical guides like compact creator kits.
Gather user reactions via public threads (Reddit, Twitter/X before 2024 transitions, Bluesky) but validate with multiple sources to avoid bias.

5. Use policy and legal templates

Adapt provisions from the EU AI Act or state-level statutory language for mandates on transparency and audits. This gives your policy ask legal grounding.
Draft phased compliance schedules (e.g., small platforms get 24 months; large platforms 6–12 months) to rebut "stifling innovation" critiques. Practical rollout and compliance considerations for edge and serverless deployments are discussed in resources like serverless edge compliance guides.

Formatting and rhetorical advice for the persuasive essay

Follow these pragmatic writing tips to maximize clarity and persuasion.

Structure and clarity

Lead with a concise thesis sentence in your intro—state the policy direction and the three core reasons supporting it.
Use topic sentences at the start of each paragraph so readers can scan your logic quickly.
End with a policy 'ask' section that lists 3–5 concrete recommendations; this is where graders and policymakers look first.

Evidence use

Prioritize primary sources and recent reports (2024–2026). Use news only for industry trends and peer-reviewed work for claims about harms.
Incorporate a short case study—e.g., a maker who lost income when synthetic episodes mimicking their style surfaced—to humanize the argument.

Tone and counterarguments

Stay professional and empathetic—acknowledge the need for innovation while pressing for accountability.
Include a strong rebuttal paragraph to the most common opposition points (economic burden, enforcement difficulty). For rebuttal framing and pitching to larger outlets, see templates like pitching to big media.

Sample citations and quick bibliography (2026-aware)

Use these as starting models; verify formatting with your required style guide.

Fink, Charlie. "Holywater Raises Additional $22 Million To Expand AI Vertical Video Platform." Forbes, 16 Jan. 2026.
Office of the Attorney General. "Investigation into Nonconsensual AI Sexualized Content." State Press Release, Jan. 2026.
Search Engine Land. "Discoverability in 2026: How digital PR and social search work together." Jan. 16, 2026.
European Commission. "AI Act Implementation Update." 2024–2026 briefing documents.

Practical checklist before submission

Run a plagiarism scan and fix any unintentional overlaps.
Confirm every factual claim has a source; mark anything speculative.
Include a definitions box if you use technical terms like "provenance metadata" or "synthetic performer."
Ask a peer or tutor to read for clarity and counterarguments you missed.

Advanced strategies (for competitive essays and policy briefs)

Want to push further and make your essay stand out?

Incorporate a short data appendix showing recommendation disparities between synthetic and human content using platform sampling.
Include quotes from creators and platform engineers to show multiple stakeholder perspectives.
Propose pilot regulatory language and an evaluation metric (e.g., reduction in nondisclosure incidents per 100k episodes after 12 months).

Final notes on voice and integrity

When writing about AI ethics and emergent media formats like vertical video, the strongest essays combine up-to-date industry examples, legal and technical grounding, and a clearly articulated moral framework. Cite credible sources, show where evidence is limited, and offer policies that are implementable—not just aspirational.

Call to action

If you found this annotated sample essay useful, download our editable template, get personalized feedback, or request a tutoring session to adapt the argument for your assignment or policy brief. Protect your grades and help shape better AI policy—start your draft today and book a 30-minute review with our editors.

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