In the race to feed AI’s insatiable appetite for training data, model builders are increasingly butting heads with the platforms that host the content they depend on. The latest flashpoint is Reddit’s lawsuit against Perplexity AI, which accuses the company of “industrial-scale” evasion of anti-scraping protections and the indirect harvesting of Reddit posts through search engine caches. The case raises a knotty question: When is public web content a legitimate training resource, and when is it legally and/or ethically off-limits?
Responses are arriving from both the marketplace and governments, with emerging startups helping content creators monetize AI-harvested data and Europe advancing the Artificial Intelligence Act, which would require firms to disclose or summarize copyrighted training data. The members of the Senior Executive AI Think Tank bring a practical and experienced perspective to the discussion of what responsible data acquisition should look like. Here, they break down where ethical and legal lines should be drawn and what responsible access must entail for AI developers, and they share insightful tips to help platforms rethink their data-licensing and access-control strategies.
“Once rate limits, robots.txt rules, paywalls or API terms are in place, the ethical default becomes ‘permission required.’”
Using Content Without Consent Isn’t Innovative; It’s Unethical
When it comes to AI firms collecting training data, Dileep Rai, Manager of Oracle Cloud Technology at Hachette Book Group (HBG), draws a firm line between the technologically possible and the ethically appropriate, asserting that “AI firms should treat public web content as usable for training only when access is both technically allowed and clearly intended by the platform.”
He stresses that the existence of a public URL does not imply consent.
“Once rate limits, robots.txt rules, paywalls or API terms are in place, the ethical default becomes ‘permission required,’” he says.
For Rai, scraping content from such sites without consent isn’t innovation; it’s extraction. He adds that AI developers must respect creator intent, be able to prove lawful access, and maintain clear data provenance.
Rai does see a path forward for platforms like Reddit seeking to protect both their content and contributors.
“A stronger approach is to shift from anti-scraping defenses to transparent, tiered licensing that protects users, supports research and enables responsible commercial use.”
Companies Must Protect Their Most Valuable Asset
Subba Rao Katragadda, Senior Principal Data Engineer at Johnson & Johnson, emphasizes that enterprises must think of their content and data as critical—and precious—infrastructure.
“Data is the world’s most valuable asset,” he says. “Companies need to protect their data through anti-scraping techniques such as auth walls, IP address reputation, user agents and JavaScript challenges.”
Katragadda notes that AI developers will inevitably scrape what’s openly available, so platforms and businesses need to decide exactly what they’re comfortable placing in that category. Such clarity reduces gray areas for everyone involved.
“AI will scrape available public web content,” he says. “Companies need to categorize the explicit data that can be accessed by AI.”
“Circumventing anti-scraping measures isn’t a gray area anymore; it’s corporate trespassing, and no firm should be doing it.”
Anti-Scraping Measures Must Be Respected—But They’re Not Enough
Serial entrepreneur and enterprise AI strategist Jim Liddle sees a tightening legal landscape where casual scraping is quickly becoming a liability, stressing that just because content is visible doesn’t mean it’s licensable for commercial AI training. The safer route, he argues, is simple: Pay for what you need.
“Companies that host content are willing to sell, so AI firms should negotiate before they scrape,” he says. “Licensing deals are cheaper than class-action lawsuits.”
Liddle notes that platforms have already built stop signs that AI firms should respect, because coasting through them comes with consequences.
“If there’s a robots.txt on a site, there’s a reason,” he says. “Circumventing anti-scraping measures isn’t a gray area anymore; it’s corporate trespassing, and no firm should be doing it.”
He cautions AI builders that the law is no longer inclined to view content scraping with an indulgent eye focused on enabling innovation.
“Fair use is definitely narrowing,” Liddle says. “Recent high-profile cases suggest courts view AI training as commercial transformation, not protected use.”
But he also warns platforms not to rely solely on robots.txt.
“Content firms should require login for full content and make scraping technically harder and legally clearer by updating their terms of service,” Liddle says.
User-Generated Content Requires Extra Care—and Clear Rules
Roman Vinogradov, VP of Product at Improvado, urges AI teams to approach public web content with caution.
“If the data is user-generated, it’s wise to prioritize ethical considerations and legal compliance,” Vinogradov says. “Always assess licensing agreements and terms of service for any potential restrictions.”
When builders are faced with ambiguity, he recommends taking the safest route, whether that’s seeking permission or exploring alternative datasets that are explicitly labeled for use.
Turning to content platforms, Vinogradov recommends rethinking data-licensing strategies by creating clear guidelines on how third parties can access content responsibly. He also notes that monetizing access to valuable data may be an option.
“Implementing tiered access models could be beneficial—allowing AI firms to pay for higher levels of access while respecting user privacy and rights.”
Key to any strategy adopted, Vinogradov says, is being open with both users and potential AI clients.
“Transparency about data usage will build trust and potentially open up new revenue streams through licensing deals.”
The Public-Private Boundary May Not Be Where You Think It Is
Uttam Kumar, Engineering Manager at American Eagle Outfitters, stresses that the term “public data” is too often misunderstood.
“AI firms should only view public web content as a viable training asset when it is demonstrably acquired in compliance with the source’s explicit terms of service and anti-scraping policies,” he says.
Kumar stresses the importance of respecting platforms’ proprietary rights to aggregated data and UGC copyrights. Even when content is freely accessible via a browser, it may still be protected.
“The public nature of data ends where the proprietary architecture begins.”
Fair Practices and Anti-Circumvention Laws Are Shaping the Future of AI Training
Chandrakanth Lekkala, Principal Data Engineer at Narwal.ai, sees Reddit’s lawsuit as emblematic of a broader reckoning.
“Reddit’s legal action underscores core conflicts in AI development,” he says. “Companies must approach publicly available online material carefully, as questions persist regarding large-scale data collection, crawler protocols and intellectual property limits.”
Lekkala explains that ethical AI development requires respecting creator intent and platform terms. And that’s not just his expert opinion; it’s a stance that’s increasingly backed up by law.
“The emerging consensus suggests public content isn’t automatically fair game; legitimate training requires balancing open access traditions with creator rights, contractual obligations and anti-circumvention laws governing technical protective measures,” Lekkala says.
Even with legal protections, he says, content creators must recognize marketplace realities and take active steps to protect themselves.
“Platforms like Reddit should implement clearer licensing frameworks, technical controls beyond robots.txt and compensation models for commercial AI use.”
“The next competitive edge in AI is not raw scale but the credibility of the data supply chain. The architects of accountable data ecosystems will set the trajectory of global AI power.”
Building Ethical, Transparent Data Supply Chains Will Become a Competitive Edge for AI Builders
Aditya Vikram Kashyap, Vice President of Firmwide Innovation at Morgan Stanley, notes that platforms like Reddit can’t just rely on expanding legal protections and the goodwill of AI builders—they must adopt a stronger defensive posture.
“They must shift from passive hosts to active data sovereigns,” he says. “It’s essential to design markets that price user knowledge, enforce transparent access rules, and protect community intent.”
As for AI builders, Kashyap argues for respecting the hard work of creators. “Public web content becomes a legitimate training asset only when creators retain agency, consent and compensation,” he says.
Violating that principle, he warns, puts companies at risk.
“Anything acquired through circumvention turns data into regulatory tinder and weakens the very systems it trains,” Kashyap says.
He adds that developing ethical, transparent data sourcing practices isn’t just an essential risk management step; it’s a winning strategy.
“The next competitive edge in AI is not raw scale but the credibility of the data supply chain,” he says. “The architects of accountable data ecosystems will set the trajectory of global AI power.”
The Legal Risks of Taking Shortcuts Are Real
Bhubalan Mani, who leads supply chain technology and analytics at GARMIN, warns that AI companies taking shortcuts may be building systems on shaky ground.
“Visibility doesn’t equal viability; acquisition itself can be infringement,” he says. “The regulatory environment is crystallizing around transparency—governments mandate training data disclosure, setting a compliance baseline.”
As many AI creators know all too well, the legal risk isn’t theoretical.
“There’s liability attached to how you acquired the data, and settlements show that copyright remains enforceable,” Mani says. “Courts are asking, ‘Did your access violate protective measures?’”
He believes platforms monetizing data through deals can capture revenue, but he notes that solutions require architecture-level sovereignty.
“Blockchain-verified provenance and tiered licensing transform governance from reactive to proactive, and federated systems where terms travel with datasets can embed accountability.”
Mutual Respect Will Determine Online Content’s Impact on AI’s Evolution
For Raghu Para of Ford Motor Company, the future of AI hinges not just on data scale but on the integrity of its acquisition. He argues that scraping isn’t a neutral act.
“Public web content isn’t a free-for-all—it’s shaped by human effort, social contracts and platform governance,” he says. “AI firms must abandon opportunistic scraping in favor of responsible stewardship, treating data as a resource that requires consent and accountability.”
He warns that Reddit’s lawsuit against Perplexity signals a deepening rift between content originators and AI developers. Bridging that rift will require commitment and decisive action from both sides.
“To move forward, AI companies must adopt transparent licensing and ethical data practices, while platforms like Reddit must replace brute-force anti-scraping with structured, value-aligned access models,” Para says.
He concludes that online content has an essential role to play in AI’s evolution—if builders and platforms work together.
“The web’s future as a learning substrate must operate on mutual respect, not extraction.”
Navigating Legal—and Ethical—Data Use Practices
- Treat access signals as consent signals. If robots.txt rules, paywalls or API terms are present, the default is “permission required”—ethical training means respecting those boundaries.
- Protect your data as you would any business-critical asset. Strong anti-scraping controls, from auth walls to IP reputation tools, help ensure only intended data is surfaced to automated crawlers.
- Don’t rely on visibility as a legal defense. Content being publicly reachable doesn’t make it licensable; negotiating access is cheaper and far safer than facing potential litigation.
- Handle user-generated content with heightened care. Always check licensing terms and seek permission—or use explicitly authorized datasets when restrictions are unclear.
- Remember that “public” doesn’t mean “unprotected.” Even browser-accessible material may fall under proprietary rights or platform-level protections that limit AI training use.
- Balance openness with compliance and creator intent. Public content isn’t automatically fair game; aligning with contractual obligations and anti-circumvention laws is essential.
- Build transparency and credibility into your data supply chain. Ethical sourcing, clear provenance and creator consent aren’t just good practice—they’re emerging competitive differentiators.
- Avoid shortcuts that undermine your legal standing. Courts increasingly ask how data was obtained, making provenance verification and compliant acquisition critical to mitigating risk.
- Adopt access models rooted in mutual respect. Sustainable progress requires AI firms to embrace responsible licensing and platforms to offer structured, value-aligned access paths.
The Future Belongs to Builders Who Play by the Rules
Responsible data acquisition isn’t a compliance burden—it’s a competitive advantage. As courts, regulators and creators push for transparency, AI builders who earn their data rather than extract it will be better positioned to scale sustainably and withstand legal scrutiny. At the same time, platforms like Reddit have a chance to transform from reluctant gatekeepers into proactive stewards of user knowledge, creating systems that protect contributors while enabling high-value enterprise partnerships.
The industry is moving toward a future where training data carries provenance markers, licensing frameworks resemble marketplaces, and collaboration replaces the arms race of evasive scraping and defensive blocking. If platforms and AI developers can align on rules rooted in consent, clarity and compensation, the web can continue powering AI’s next breakthroughs without compromising creator rights or platform trust.
