Amazon’s lawsuit against Perplexity rattles AI&driven search — and puts OTAs, hotels, and travel suppliers on alert

Amazon’s lawsuit against Perplexity intensifies scrutiny of AI-generated answers and data sourcing, with knock-on effects for how travel demand is identified and directed online.

By HNR News Staff Reporter

Amazon’s lawsuit against Perplexity intensifies scrutiny of AI-generated answers and data sourcing, with knock-on effects for how travel demand is identified and directed online.

• OTAs and metasearch players face a higher risk that AI assistants will compress the funnel and divert referral traffic, reshaping the economics of performance marketing.
• Hotels, airlines, and other suppliers could gain leverage in direct distribution — but face new content licensing and brand safety questions.
• Analysts and company filings have long warned that shifts in search and discovery can materially impact traffic and acquisition costs.

Why this lawsuit matters for travel

Amazon’s legal action against Perplexity lands squarely in the fight over how AI systems source, synthesize, and present information. For travel, where search and discovery are tightly linked to conversion, the case could accelerate changes already underway: AI assistants that answer trip-planning queries upfront, fewer clicks to traditional sites, and a reshuffling of who controls demand at the top of the funnel.

OTAs, metasearch platforms, and suppliers have built businesses around a predictable mix of paid search, SEO, branded demand, and app loyalty. If the lawsuit pushes AI companies toward stricter data licensing, clearer attribution, and link-out requirements, incumbent players could retain more visibility. If instead the ecosystem embraces more “answer engines” with minimal linking, referral economics could deteriorate further.

OTAs’ traffic risk moves from theoretical to tangible

Booking Holdings, Expedia Group, and Tripadvisor have repeatedly warned in filings that shifts in search dynamics can dent traffic and returns on ad spend. Booking Holdings’ 2023 Form 10-K states: “We rely on a number of marketing channels, including search engines, to drive traffic to our platforms,” adding that changes in search algorithms and market dynamics could “adversely affect” performance marketing efficiency. Expedia Group’s 2023 Form 10-K similarly notes: “Search engines frequently update and change the logic that determines placement and ranking of results,” cautioning that traffic, costs, and results could be negatively affected.

The Amazon–Perplexity case focuses on how AI systems gather and present content—the very layer that shapes query flows. If courts or settlements push toward explicit licensing and more prominent linking, OTAs could preserve high-intent traffic. If not, AI overviews and assistants may intercept more itinerary and lodging queries before users ever reach an OTA or metasearch site.

Metasearch and performance marketing economics are in flux

Metasearch firms depend on paid placements and deep link-outs to merchants and OTAs. AI assistants that present a short list of “best options” without prominent sources could compress auction demand and erode click-throughs. That, in turn, could pressure cost-per-click bidding and the conversion math OTAs use to justify rising customer acquisition costs.

Conversely, if outcomes of this case lead to stronger attribution norms — for instance, mandated citations, source links, or pay-for-content frameworks — metasearch players could regain a more durable role as trusted, attributable sources for prices and availability that AI systems are compelled to reference.

Hotels and airlines: potential gains in direct, with new obligations

Direct sellers may benefit if AI assistants privilege “first-party, freshest data” for inventory and policies. Hotels and airlines with robust APIs, accurate content, and clear rate rules could surface more often in AI-generated summaries, supporting direct bookings and loyalty sign-ups.

But there’s a flip side. If AI systems require licensed datasets to avoid legal exposure, suppliers may need to negotiate data access and usage terms—not only with OTAs and metasearch partners but also with AI platforms. That could add a new layer of commercial complexity on top of existing parity, distribution costs, and brand-safety concerns (for example, misattributed reviews or outdated cancellation rules in AI snippets).

Content rights, licensing, and attribution move center stage

A central question in the lawsuit — how AI models source and reuse web content — echoes industry-wide debates. Travel brands invest heavily in photos, descriptions, maps, reviews, and policy text. If AI assistants scrape, train on, and paraphrase that content without clear licensing or links, suppliers and intermediaries lose both traffic and credit for the content that drives conversion.

Expect more contracts to address: – Training data permissions and model outputs – Update frequency and recrawl cadence for time-sensitive travel information – Citation, deep-link, and brand usage standards in AI answers – Liability and corrections when AI presents inaccurate rates or policies

Pricing transparency and consumer trust

AI-generated answers that summarize fees, availability, or fare classes carry reputational risk. If outputs omit resort fees or present stale availability, brands take the blame. Stronger provenance and real-time data access are likely to become table stakes for any AI travel assistant claiming authoritative recommendations.

This dynamic could advantage suppliers and platforms with the cleanest data pipelines and governance. It also makes a case for industry-backed schemas and APIs that AI companies can license, rather than relying on opportunistic scraping.

What this could mean for Google, Amazon, and the travel ad market

If AI assistants reduce outbound clicks, advertisers will seek guaranteed visibility elsewhere, including retail media networks and closed ecosystems. Amazon’s legal posture may signal a preference for licensed, attributable content flows within its own properties and partner sites. For Google — the most significant traffic source for many travel brands — ongoing evolution of AI Overviews already pressures traditional SEO and SEM strategies; further legal clarity on AI content usage could determine how much link equity remains in the system.

How industry leaders are positioning

– Booking Holdings has emphasized mix-shifting to direct and app usage while investing in AI-driven trip planning and customer service, a strategy meant to reduce dependence on volatile traffic sources.

– Expedia Group has rolled out AI-powered shopping and service tools, aiming to improve loyalty and conversion so paid traffic pays back faster, even if click volumes change.

– Major hotel groups have pushed direct-booking benefits and better first-party content controls, seeking to ensure their official rates, policies, and imagery are the sources AI systems reference.

As Booking’s 10-K notes, reliance on external platforms for demand can “adversely affect” results when algorithms or formats change — a reminder that legal and platform shifts in AI search are not side issues but core distribution risks.

What to watch next

– Any court guidance or settlements that define acceptable AI training, scraping, and link attribution practices

– Whether AI platforms strike licensing deals for travel content, inventory data, and reviews — and on what economic terms

– Changes in Google’s AI Overviews and assistant products affecting organic and paid visibility for OTAs and suppliers

– KPI signals: branded versus non-branded traffic mix, app adoption, metasearch CPCs, and conversion rates on AI-referred clicks

– Supplier responses: API quality upgrades, content governance, and co-marketing agreements with AI platforms

The bottom line: the Amazon–Perplexity case establishes data provenance and attribution on a legal footing that could meaningfully alter how travel demand is generated and measured. OTAs risk further funnel compression; suppliers may win more direct exposure — but only if they can license, structure, and safeguard their content and data in a way AI systems must respect.