How could regulation shape advertising within large language models by 2030?

Response from OpenAI’s ChatGPT:

Here’s how I see regulation likely shaping the ways advertising inside or through large language models (LLMs) by 2030 — the levers regulators might pull, trade-offs, and what that could mean for marketers, platform providers, and users.

Existing Foundations & Signals

These existing laws/regulations already give hints of where things are headed:

EU’s AI Act — includes prohibitions on “subliminal, manipulative or deceptive techniques” in AI, and requires disclosure of AI-generated or manipulated content.
Transparency & Targeting of Political Advertising regulation in the EU — rules for targeting and disclosures of political ads.
Local laws and codes around advertisement disclosure of AI usage (for example in the UK) and rules about misleading or false claims.

These are early but growing precedents that will guide what’s possible in LLM-driven environments.

Key Regulatory Axes for Advertising in LLMs by 2030

By 2030, regulation around ads in LLMs will likely evolve along several dimensions. Below are major axes and what regulatory action in each might involve.

Regulatory Dimension	Possible Rules / Legal Requirements	Implications for Advertising in LLMs
Disclosure / Transparency	• Any ad content generated by an LLM (or suggestions/recommendations by it) must be clearly labelled (e.g. “Sponsored”, “Advertisement”, “Recommendation by Brand X”). • If content uses synthetic generation (images, voices, manipulated visuals), must indicate that. • Disclosures when personalization or profiling is used.	Marketers must design with transparency in mind; failure to do so would risk legal penalties. Advertising inside model outputs will need clear markers. It may limit “blend-in” or native ad styles where the ad is hard to distinguish from model’s unbiased content.
Limits on Manipulation / Exploitation	• Bans or strong limits on targeting or messaging that exploit vulnerabilities (age, mental health, socio-economic status, minors). • Restrictions on subliminal messaging, emotional manipulation, or deceptive “persuasive” prompts that steer users without awareness. • Rules around how data is used to infer psychological or emotional states (possibly privacy law overlaps).	Advertisers and LLM operators must avoid “dark patterns” or messaging that nudges in non-transparent or unfair ways. Some high-risk advertising styles may be prohibited or heavily constrained.
Privacy & Data Use / Profiling	• Stronger restrictions on how user data can be collected, stored, and used for ad targeting inside LLMs. • Consent requirements for profile building; limits on using sensitive data categories. • Perhaps special rules for profiling that influences “significant decisions” (purchases, beliefs, political or health advice).	Personalization will need to operate under stricter guardrails; more opt-ins; possibly more user control over what targeting they accept. Could reduce the precision of ads or add friction.
Fairness, Non-Bias, and Safety	• Requirements that models be audited for bias in how they serve ads or recommendations (e.g. avoiding discriminatory targeting). • Ensuring that ad content doesn’t propagate harms (misinformation, defamation, unfair claims). • Safe-guards against deepfakes or synthetic content used maliciously in ads.	LLM ad content pipelines will need moderation, oversight, and auditing. Brands or platforms might need to certify compliance. More “safe mode” defaults.
Consumer Protection & Misleading Claims	• Tight rules for truth in advertising when LLMs give “advice” or “recommendations”—disclosure of sponsored content, affiliate relationships. • Rules preventing ads that misrepresent capabilities of a product; any claim must be substantiated. • Possible class actions or enforcement actions for harms arising from misleading AI-driven suggestions.	Marketers will need more rigorous evidence backing claims. Legal risk for claims made via AI that mislead users. Likely more pre-clearance of ad content.
Regulatory Oversight, Audits, & Accountability	• Platforms and model deployers required to maintain logs of what ads are served, how targeting was done, how the ads were generated. • Third-party or government audits, possibly regular reporting of ad performance, demographics. • Fines & penalties for non-compliance, perhaps tied to global turnover. • Requirements for watermarking or metadata embedded in synthetic content so that it can be traced.	Advertising operations inside LLMs will need more internal governance, compliance infrastructure. Smaller players may struggle with costs. Watermarking or traceability will need to be built in.
Special Rules for Sensitive Domains	• Stricter rules in areas like political persuasion, health, finance, minors etc. • May require special licences or “high risk” classification of LLMs serving certain content. • Possibly separate rules for “influencer-like” LLM outputs vs purely informational ones.	Marketers in these domains will face greater scrutiny. For example, health advice or political recommendations embedded in LLM chat might need certification or oversight. Could limit what LLMs are allowed to do in those areas.
Platform Liability & Gatekeeping	• Platforms hosting or operating LLMs may be held responsible for the ad content (misinformation, deceptive ads). • Rules forcing platforms to have moderation and content removal processes. • Oversight on which advertisers are allowed, ad review requirements.	Platforms may impose stricter ad submission & review processes. Possibly higher barriers to entry for advertisers. Legal risk pushes platforms to be conservative.

Scenarios: What Regulation Could Look Like in Practice by 2030

To make this more concrete, here are a few plausible scenarios for how regulation might actually manifest in the LLM ad space by 2030.

Scenario	Key Features / Rules	Pros & Cons
Scenario A: “Strong Regulation + High Transparency”	– All ads in LLM outputs must be clearly labelled, including “sponsored content”, “AI generated” etc. – Targeting based on personal data requires explicit opt-in; sensitive attributes (age, health, finances) restricted. – Watermarking or metadata tagging of synthetic content. – Platforms must provide users with “why was this ad shown” info. – Heavy penalties for misuse or for failure to disclose.	Pros: Builds trust; protects consumers; limits worst abuses. Cons: Slower innovation; higher compliance costs; possibly less precise ads; smaller players struggle.
Scenario B: “Risk-Based / Sectoral Regulation”	– General rules more permissive, but stricter for “high risk” sectors: politics, health, finance, children. – For less risky consumer goods, looser regulation but still disclosure required. – Cross-jurisdiction variation: some countries are stricter.	Pros: More flexible; allows innovation in lower risk areas; more tailored oversight. Cons: Complexity, patchwork regulation; risk of loopholes; harder for multinational brands.
Scenario C: “Minimal Regulation / Self-Regulation with Nudges”	– Mostly industry codes and self-regulation (e.g. platforms agreeing to disclose AI content, but lighter enforcement). – Some legal requirements, but enforcement lax; heavy reliance on consumer backlash. – Some standard setting (e.g. watermarking) but not universally enforced.	Pros: Faster innovation; less cost overhead; more experimentation. Cons: Risk of abuses; erosion of trust; possible backlash or sudden regulatory clampdowns; reputational risk for brands.

Likely Challenges & Unintended Consequences

Regulation doesn’t work in isolation; enforcing it in LLM-driven advertising will present challenges and trade-offs.

Defining “advertising,” “recommendation,” “sponsored content”: In a conversational system, what counts as an ad vs. a “helpful suggestion” may blur. Regulators will have to define these clearly.
Enforcement across borders: LLM providers often serve globally; laws vary by country. Ensuring compliance in every jurisdiction is complex.
Over-regulation stifling innovation: If rules are too strict or too costly, startups might drop out; only big players can comply.
User experience vs. regulation: Disclosures and transparency are good, but too many interruptions or notices might degrade UX. Finding balance is key.
“Label fatigue”: If every minor thing is labelled “Sponsored by …” or “AI-generated,” people may stop noticing or caring, defeating purpose.
Technical difficulties: Watermarking, auditing, proof of synthetic content or origin may be hard for some models. Also, privacy preserving personalization may limit ability to explain ad targeting.

What Marketers & Platforms Should Do to Prepare

Given these likely regulatory trends, marketers and platform providers will be well-served by:

Building transparency by default: All ad / recommendation content, especially AI-generated parts, should be designed with disclosure.
Privacy-centric design: Minimize use of sensitive data; get explicit consent; use aggregate or anonymized data where possible.
Ethical oversight & auditing: Have internal audit processes for ad content, bias, misleading claims; use external audits for trust.
Clear documentation & traceability: Maintain logs of ads generated/recommended, what data & prompts used, so that compliance or investigations are possible.
Segmented approach for sensitive sectors: If you’re in health, finance, politics, be extra cautious; likely you’ll need to comply with stricter rules.
Engage with regulators / standards bodies: Help shape norms and codes of practice; early adaptation helps avoid shock when regulation comes.

Sources include: TaylorWessing, EU Artificial Intelligence Act, Wikipedia and FCC[dot]gov