Introduction

The rapid rise of large language models (LLMs) like ChatGPT has sparked questions about the future of advertising models built on traditional internet usage. Since ChatGPT’s public launch in November 2022, millions of users have begun turning to AI chatbots for information and answers. This report analyses how mass AI adoption – particularly user-friendly LLMs – is affecting the advertising-driven business models of Google (built on search ads) and Meta (built on social and display ads). It also evaluates the broader impact on the digital ad market and explores whether this shift opens new opportunities for video streaming platforms (AVOD, SVOD with ads, and hybrid models) to capture ad spend. Major global markets (US, UK/Europe, APAC, LatAm, MENA) are considered in this analysis. Key statistics and forecasts are presented in tables for clarity, with strategic insights for an executive audience in the streaming industry.

Google’s Advertising Revenue Trends (2020–Present)

Google’s advertising business has grown substantially since 2020, albeit with varying annual growth rates. Table 1 below shows Google’s ad revenue from 2020 through 2024, illustrating a pandemic-era surge followed by a recent slowdown:

Table 1. Google Advertising Revenue, 2020–2024 (Worldwide)​

Alphabet’s management attributed these late-2022/early-2023 dips primarily to a volatile digital ad market and macroeconomic pressures (e.g. advertisers pulling back), but the timing coincided with LLMs emerging as a new competitor for user attention.

By late 2023 and 2024, Google’s ad business regained momentum. Search advertising and other Google services returned to growth as the ad market stabilised. For example, in Q4 2024 Google’s ad revenues reached $72.5 billion (up 10.6% YoY) – the company’s largest-ever quarter​. Full-year 2024 ad revenue rose by an estimated 11% (to ~$264B), indicating that Google’s core ad business remains robust.

Regionally, growth has lately been fuelled by APAC advertisers – Alphabet noted that a strong Q4 2023 in Asia-Pacific retail ad spend boosted results. In contrast, the U.S. and European markets, while large, have matured and showed slower growth. Overall, through 2024 Google maintained its dominance in search advertising, but the post-ChatGPT era growth rates are notably lower than the pre-2022 boom, reflecting both a tougher economy and the first signs of market saturation (or disruption). The question is how much of the slowdown (if any) can be attributed to users shifting some queries to AI tools like ChatGPT instead of Google search.

Rise of LLMs: Usage Statistics and Search Behaviour Shifts

ChatGPT’s launch in late 2022 ushered in an explosion of AI usage by consumers. ChatGPT amassed 100 million users within two months of launch, making it the fastest-growing consumer application in history​

. By January 2023 (two months in), the chatbot was seeing about 13 million unique visitors per day and over 590 million total visits in that month alone​

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. This adoption far outpaced previous tech platforms (for comparison, TikTok took ~9 months and Instagram ~30 months to reach 100 million users)​

. Usage continued to climb: at its peak in May 2023, the ChatGPT website received an estimated 1.8 billion visits (monthly), before leveling off in mid-2023​

. During the summer, traffic dipped (e.g. ~1.43 billion visits in August 2023) as novelty wore off and students were on break​

. By late 2023, ChatGPT stabilized at around 180 million monthly unique users globally​

– an enormous user base for a tool that didn’t exist a year prior.

Crucially, many ChatGPT interactions replace tasks that might have started on Google Search in the past. Users ask the AI questions, advice, even product recommendations. A recent analysis found that roughly 30% of ChatGPT prompts resemble “traditional” search queries (e.g. informational questions), although not all use the web​

. Even when ChatGPT is used for search-like purposes, however, it represents a small fraction of overall search volume. Google processes an estimated 14 billion searches per day, or over 5 trillion searches per year​

. By comparison, ChatGPT was handling on the order of 37.5 million search-like queries per day in 2024​

. In other words, ChatGPT’s query volume was only about 0.25% of Google’s – despite the hype, it’s not (yet) siphoning off massive search traffic​

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. Furthermore, Microsoft’s integration of GPT-4 into Bing (launched Feb 2023) has had minimal impact on market share: in the year after adding AI chat, Bing’s global search share rose only from ~2.8% to ~3.4% (a gain of <1 percentage point)​

. Google still holds about 90+% of worldwide search engine usage, and even in the U.S. remains ~88–90% vs. Bing’s ~6–8%​

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Early evidence suggests that LLMs have not yet caused a significant decline in Google’s search traffic. In fact, Google’s internal data indicated search volumes continued to grow through 2023-2024. Alphabet’s CEO Sundar Pichai noted that offering new AI features in Search (e.g. experimental Search Generative Experience) actually led to more user engagement in some cases​

. An independent study by Sparktoro/Datos found Google search queries increased ~21.6% in 2024 vs 2023, despite ChatGPT’s presence​

. So, traditional search remains entrenched for now – habits don’t change overnight, and many users still “Google” for quick answers out of convenience. However, the quality of search traffic may be shifting: by 2024 an estimated 60% of Google searches ended without a click to any external website​

(users either got their answer from the results page or abandoned), a trend possibly accelerated by richer AI snippets and direct answers on the search page.

Key LLM usage stats:

• ChatGPT reached 100M users in 2 months (Jan 2023)​

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  , and ~180M monthly users by late 2023​

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• ChatGPT usage vs. Google: ~1 billion ChatGPT messages/day vs 14 billion Google searches/day in 2024 (ChatGPT ~0.25% of search query volume)​

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• Bing’s market share (global): ~4% in 2024, up only slightly after adding AI​

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  . Google retains ~93% share of search queries​

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• Search engine usage outlook: Gartner predicts that “by 2026, traditional search engine volume will drop 25%” as users turn to AI assistants​

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  . This is speculative but underscores the perceived long-term impact of AI on search behavior.

In summary, LLM adoption has grown explosively, but it has not yet materially eroded Google’s search dominance in the aggregate. Many users experiment with ChatGPT for complex Q&A or creative tasks, while still relying on Google for high-frequency needs (simple queries, navigation, etc.). The true threat to Google’s model may manifest over the next several years if AI chatbots become integrated into daily workflows and siphon off the high-value queries that drive ad clicks.

Impact on Google’s Ad Business: Early Signs and Correlation Analysis

Given the data above, any direct correlation between LLM usage growth and Google’s ad revenue downturn is hard to conclusively identify so far. Google’s advertising growth did slow markedly in late 2022 and early 2023, right after ChatGPT’s debut – but the evidence points to macroeconomic and industry factors as the primary causes, rather than an immediate AI-driven user exodus. Notably, in Q4 2022 Google’s ad revenue fell 3.6% YoY​

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, and Q1 2023 also saw a decline​

. However, this period coincided with high inflation, reduced marketing budgets, and tough comparisons to the previous year’s COVID bump. There was no sudden collapse in Google usage; in fact, Google’s total search queries were still rising in early 2023, even as ad revenue dipped. This suggests advertisers cut spend for economic reasons, not because users vanished. As one analysis noted, “there’s been a lot of noise surrounding ChatGPT’s impact on Google’s revenue… but Google’s search market share has been consistently stable”​

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From late 2023 into 2024, Google’s ad growth actually reaccelerated despite further increases in LLM adoption, reinforcing the weak short-term correlation. By Q4 2023, Google’s advertising had resumed growth (helped by holiday and APAC retail demand), and in 2024 search ad revenue hit new records​

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. In other words, Google hasn’t (yet) experienced a sustained downturn attributable to AI chatbots. Even Gartner’s forecasted 25% drop in search volume by 2026 is not evidenced in current data – it remains a forward-looking scenario​

. As of 2024, many search marketers and analysts find “no evidence that vast numbers of searchers are abandoning Google for ChatGPT”​

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That said, there are qualitative signs that LLMs are starting to encroach on Google’s search value proposition in specific niches. For example: tech-savvy users (software developers, students, etc.) report using Google much less for coding problems or homework help, preferring ChatGPT’s direct answers. Such anecdotes abound (e.g., forum discussions where individuals claim an “80% drop” in their Google Search usage since using ChatGPT). If these behaviors expand beyond early adopters, Google could eventually see a real impact. Additionally, the nature of Google’s search results is changing due to AI. Google is rolling out its own AI “Search Generative Experience” (SGE) which provides synthesized answers at the top of results pages. While this is meant to keep users satisfied on Google, it might reduce clicks on paid ads if users get what they need from the AI summary. In the long run, if Google’s search becomes more of an answer engine (like an LLM) and less of a referral page of links, the company will need to find ways to integrate advertising into AI-driven results. Google is already experimenting here – e.g., showing sponsored links within AI answers or allowing product placements in chat responses​

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. The goal would be to maintain monetization even if user behavior shifts from clicking links to conversing with an AI.

In summary, there is no clear, immediate downturn in Google’s ad revenue attributable to ChatGPT or LLMs so far – any correlation is overshadowed by other factors. Google’s search ad machine in 2023-24 is still growing (albeit at a slower pace)​

. However, the threat is real in the medium to long term. If LLM usage continues its upward trajectory and if AI chat becomes a preferred interface for a significant share of queries, Google’s core search ads business could face structural headwinds. This makes scenario planning crucial, as explored next.

Google Ad Revenue Outlook: 1, 2, and 5-Year Scenarios with Rising AI Usage

To gauge Google’s future ad revenue trajectory under the shadow of AI, we consider three scenarios: (A) Baseline (no major AI impact), (B) Moderate impact, and (C) Severe disruption. Table 2 projects Google’s advertising revenue in each scenario over the next 1, 2, and 5 years:

Table 2. Projected Google Ad Revenue Under Different AI Impact Scenarios (Illustrative)

Assumptions: Scenario A assumes Google maintains ~8–10% annual ad growth (from global GDP growth, new advertisers, and Google’s own AI adaptations keeping search strong). Scenario B assumes some search query diversion to AI – perhaps a <10% volume loss by 2026 – causing a brief revenue stall (~0%–5% growth) before Google’s innovations (integrating ads into AI answers, new ad products) restore modest growth by 2030. Scenario C assumes a major shift of user queries to AI platforms – roughly a 25% drop in traditional search volume by 2026 (per Gartner’s prediction)​

– leading to a significant revenue decline, with Google scrambling to rebuild ad revenue via new channels by decade’s end. These figures are illustrative and rounded for trend indication.

In Scenario A (Baseline), Google essentially weathers the AI hype with minimal damage. Search and YouTube continue to grow in emerging markets (APAC, LatAm, MENA where millions of new internet users come online), and AI is used as a tool to enhance search rather than replace it. Under this status quo, Google’s ad revenue could keep rising at high-single-digit percentages annually, crossing $300B+ by 2026 and approaching half a trillion dollars by 2030. This assumes Google successfully monetizes new areas (e.g. maps, e-commerce, AI-driven products) and that advertisers keep increasing budgets.

Scenario B (Moderate Impact) envisions that LLMs start to nibble at Google’s search dominance in specific domains. Perhaps by 2025-2026, a noticeable minority of users (say 10–15%) routinely use AI assistants for search-like queries (especially on desktop and among younger demographics). This could flatten Google’s search query growth or even cause slight declines in certain high-value query categories (e.g. how-to queries, long-tail informational searches). If search volumes stagnate, Google’s ad revenue might plateau around the mid-$200B range for a couple of years. However, Google would likely deploy countermeasures: for example, Google’s own LLM (Bard and the upcoming Gemini) is being integrated into search results, and the company is exploring new ad formats within those AI responses​

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. This could open alternative revenue streams (imagine sponsored recommendations directly in a chatbot answer, or transaction fees if Google’s AI completes purchases for users). By years 4–5, these adaptations could return Google to a growth path, albeit a slower one. In this scenario, Google’s ad revenue might hover around ~$265B in 2026 and perhaps climb back toward $300B by 2030, but it would lag far behind the baseline scenario. Essentially, moderate AI disruption could cost Google tens of billions in foregone revenue growth, even if not causing an absolute decline.

Scenario C (Severe Disruption) is a more dramatic upheaval. If generative AI truly triggers a new paradigm of information access, traditional search as we know it could shrink substantially. Gartner’s vision of a -25% drop in search engine volume by 2026 would likely correspond to a similar drop in Google’s ad revenues (absent mitigation)​

. In this worst case, within a couple of years Google could see its annual ad sales fall from ~$264B (2024) to the low-$200B range – essentially losing roughly $50–60 billion of annual revenue that it might have had if not for AI alternatives. The hit could be even larger if AI chatbots not only reduce search queries but also disrupt display advertising (for instance, if more users spend time in AI chat apps instead of on web pages that show Google’s network ads). By 2030, Google’s ad business might still be struggling to regain momentum, perhaps stabilizing around ~$220B if new AI monetization efforts bear some fruit. This would be a radical shift: Google would have transformed from a growth company to a flat or contracting one in its core sector. It’s worth noting that such a scenario would likely provoke major strategic shifts at Google (e.g. accelerated diversification into non-ad businesses, or a complete overhaul of the search interface to be AI-first with new ad models). Indeed, Google’s leadership is already signaling an “AI-first” future for search in 2025​

, suggesting they are preparing to defend against this disruptive scenario.

It’s important to emphasize that these scenarios are not predictions but explorations of possible futures. The actual outcome will depend on: (a) User behavior – will mass audiences truly change their long-standing search habits for AI tools? (b) Google’s response – how effectively can Google integrate AI and retain advertisers? (c) Competition – will a rival (Microsoft, or even an Apple or Amazon with new AI) seize the moment to steal share? In all likelihood, Google’s ad model in 1–2 years will see only modest impact (some slowdown but not a collapse), while the 5-year outlook carries more uncertainty. From an advertiser’s perspective, however, even a moderate slowdown at Google means billions in ad dollars potentially looking for new homes – which presents opportunities for other platforms, especially in digital video and streaming media.

Opportunities for Streaming Platforms as Ad Budgets Shift

If search advertising growth decelerates due to LLMs, those advertising budgets don’t disappear – brands will seek other channels to reach consumers. In a scenario where Google’s dominance is chipped away, video streamers and broadcasters could emerge as major beneficiaries. Streaming services (from free, ad-supported platforms to hybrid SVOD with ad tiers) are capturing ever more viewer attention, particularly on connected TVs and mobile devices. Advertisers are already following this shift in eyeballs, and any weakness in the duopoly of Google/Meta could accelerate the flow of money into streaming/CTV (Connected TV) advertising.

Digital video ad spending is on a strong growth trajectory. In the US – a bellwether market – connected TV ad spend is expected to grow ~10% annually through 2027​

. Globally, CTV advertising was about $29–30 billion in 2024, projected to exceed $38 billion by 2027​

. While this is still much smaller than global search ad spend, the gap is closing. More importantly, streaming platforms have recently “opened the gates” to advertisers in ways they hadn’t before. For instance, Netflix and Disney+ launched ad-supported tiers in late 2022, reversing a long history of purely subscription models. HBO Max (now Max), Peacock, Paramount+, and others are all pushing hybrid plans. Even regional streaming services and broadcasters in Europe (e.g. ITVX in the UK), Asia (e.g. Viu, Hotstar), Latin America, and MENA (e.g. Shahid) are expanding their ad-supported offerings to complement subscription revenue.

As user growth in some ad channels (like search or social) slows, advertisers will increasingly consider premium video streaming audiences, which are often large, engaged, and now reachable via ads. This is especially true for brand advertisers who value sight-sound-motion impact – they might reallocate budgets from search to CTV for better brand storytelling. But it can also apply to performance marketers if streaming platforms enable more targeting and interactivity. In markets like the US and UK, linear TV budgets have already been shifting into streaming as cord-cutting continues. In emerging markets (APAC, LatAm, MENA), where linear TV remains strong, streaming is often additive – but the youth-skewing audiences on mobile streaming apps are very attractive to advertisers wanting to reach those demographics that perhaps don’t use Google as much.

To truly capitalize on this opportunity, streaming platforms need to make sure they offer what advertisers (especially those potentially moving spend from Google) are looking for. Key implications and requirements include:

• Scale and Reach: Large advertisers will ask if a streaming platform can deliver millions of impressions across broad demos (as Google can). Streamers may need to aggregate inventory (e.g. via programmatic exchanges or partnerships) to offer enough scale, especially in smaller regions like MENA or LatAm where a single service might not have nationwide reach.

• Targeting and Data: One appeal of Google/Meta ads is precise targeting and rich data for measurement. Streaming platforms must bolster their ad tech – leveraging first-party data (subscription info, viewing habits) to allow targeting by demographics, geography, even interest (e.g. serving an auto ad to someone who watches lots of car shows). Addressable TV advertising – where different households see different ads on the same stream – should be a standard offering. In markets like the US and UK, addressable ads on platforms like Hulu or Sky’s AdSmart have shown the way.

• Ad Formats and Innovation: Streamers can go beyond the 30-second TV spot. Interactive ads, pause-screen ads, or shoppable QR codes on CTV ads are options. If conversational AI is popular, we might even see AI-powered ad experiences on TV (for example, a viewer could ask their TV for more info after seeing an ad). Streaming services should experiment with such formats to attract both brand and performance ad spend.

• Global vs Local Approach: In global markets, one size won’t fit all. US and Europe have heavily programmatic, data-driven ad ecosystems – streamers here need to plug into those (working with DSPs, offering real-time bidding on inventory). APAC and LatAm might have more direct sales and sponsorships still. Local content is king in many regions, so local advertisers might be drawn to domestic streaming platforms (e.g. a Brazilian bank advertising on Globoplay, or an Arab telecom on Shahid). Streamers in each region should tailor their ad sales strategy to local advertiser bases while also allowing global brands to buy easily.

Ultimately, streaming platforms have a window of opportunity to claim ad dollars that may be less concentrated on Google in the future. Those who move fast to court advertisers – especially by addressing the needs of both major brands and smaller businesses – could significantly boost their ad revenues.

Democratizing the Premium TV Ad Market for All Advertisers

One of the biggest shifts that Google and Meta brought to advertising was democratization: millions of small and medium businesses (SMBs) could advertise with small budgets and self-serve tools. Traditionally, TV advertising was dominated by a few hundred big-spending brands, due to high entry costs and manual sales processes. If streaming platforms want to siphon ad spend from the Googles and Metas, they must open up premium video advertising to a much wider pool of advertisers, including SMEs. This means adopting the structural and commercial changes that digital platforms use:

• Self-Service Ad Buying: Streaming services should enable automated, self-serve ad platforms where an advertiser can sign up, target, upload a video creative, set a budget (even a few hundred dollars), and launch a campaign – all without human sales intervention. Currently, many AVOD platforms require talking to a sales rep or committing to large buys. Opening a “Google Ads style” interface for CTV could unlock huge latent demand. According to industry analysis, there are about 9 million advertisers on search and social channels globally (spending nearly $300B) versus only ~500–600 advertisers that historically dominated TV​

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  . In other words, the vast majority of businesses have never advertised on TV but many could be interested if it were accessible​

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  . Streaming TV could be the vehicle to bring those new advertisers in.

• Lowered Entry Barriers: To welcome smaller advertisers, streamers should allow very flexible budgets (campaigns in the low thousands of dollars, not millions) and short-term buys. For example, instead of a months-long TV campaign, an SMB could run a one-week targeted burst on a streaming service. Some ad-tech providers are already enabling this. It’s reported that emerging FAST (Free Ad-Supported TV) channels and platforms are launching tools to let SMBs buy localized TV ads for as little as $500 to start​

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  . This is transformative – “Straight-to-streaming ad managers will enable SMBs to buy ads on streaming TV with localized, targeted campaigns starting as low as $500. This was never possible before – it’s a game changer that will supercharge the new television.”​

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  Streaming platforms should either build or partner to offer such low-barrier plans.

• Programmatic Marketplaces: Embracing programmatic ad selling (real-time auctions for ad slots) can democratize access. Instead of lengthy negotiations, advertisers big or small can bid for impressions in an exchange. Platforms like The Trade Desk, Google’s DV360, etc., are already channels through which advertisers buy CTV inventory. Streamers should ensure their ad inventory is available on major demand-side platforms (DSPs) with proper controls. This allows even a small local advertiser’s agency to easily include streaming in their media buy, just as they do with web or social ads.

• Better Measurement and ROI Metrics: One reason SMBs flocked to Google is they could track clicks, conversions, and get clear ROI. Streaming ads are higher in the funnel (brand awareness), but to attract performance-minded advertisers, platforms should invest in measurement tools. For example, working with third-party attribution firms to show if a CTV ad led to a website visit or store visit (perhaps via smart TV and mobile device matching). Also, offering incremental reach metrics (how many unique viewers were reached that you can’t get on linear TV) can justify the spend to advertisers.

• Creative Support and Formats for SMBs: Many SMEs don’t have TV-quality video ads readily available. Streaming platforms (or their ad tech partners) could provide creative solutions – such as simple templated video ad builders, or partnerships with production networks to make short ads cheaply. Even using generative AI to create ad variants could help SMEs produce acceptable creative. The easier it is for a small business to create and run a streaming TV ad, the more will try it.

• Geo-Targeted and Niche Content Alignment: A huge opportunity of streaming is precision. For example, an auto parts store in one city could run ads only to viewers in that city who watch a car-related show. Traditional national TV can’t do that. The rise of niche FAST channels (e.g. a “Bob Ross Painting” channel, or local news streams) means advertisers can find very contextually relevant placements​

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  . Streamers should highlight these options. As the OrkaTV analysis posits, an arts-and-crafts shop could target viewers of a painting channel in their region – something never feasible in cable TV​

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  . This kind of hyper-targeted video advertising merges the targeting of digital with the impact of TV.

In essence, streaming platforms need to transform the TV ad buying model to be more like online ads. The industry is moving this direction: executives note that connected TV can “democratize ads for smaller buyers” by leveraging programmatic tech and flexible formats​

. The value proposition to advertisers (and especially SMEs) is compelling – TV advertising is no longer just for the largest brands; it can be “for everyone.” A recent survey even indicated that viewers are more receptive to ads on TV streaming than on other media​

, meaning ads on premium content carry an inherent credibility. If streamers successfully open up to broader advertisers, they not only fill unsold inventory (many ad-supported streamers today still have unsold ad slots or “house” promos running​

), but they also bring new ad dollars into the ecosystem – potentially absorbing any budget that advertisers divert away from Google search or even social media.

For major global markets, the approach may vary: in the US, UK, and Europe, where many SMEs already use digital advertising extensively, the push might be to get those same advertisers to extend their campaigns into streaming TV via self-service tools. In APAC or LatAm, there may be more first-time digital advertisers that can leapfrog directly to streaming if given the chance. In MENA, where social media advertising is huge among local businesses, a platform like Shahid (a popular Middle East streamer) could create a self-serve portal to attract some of the thousands of local businesses that currently only advertise on Facebook/Instagram. The common thread is democratization: making premium video ad inventory as easy to buy as a Google Ad. Achieving that over the next few years could unlock a wave of revenue for streamers and provide advertisers a valuable alternative as traditional channels evolve.

Meta’s Advertising Model in the Age of AI

While Google faces direct challenges from AI chatbots in its search-centric model, Meta’s advertising business is somewhat more insulated in the short term. Meta (which includes Facebook, Instagram, WhatsApp, Messenger) relies on a mix of feed-based ads, Stories/Reels video ads, and messaging ads – these are tied to social engagement rather than explicit user queries. People use Meta’s platforms primarily to connect and consume content, which AI chatbots don’t replace (at least not yet). Indeed, Meta’s ad revenue has recently rebounded after a dip, showing resilience. In 2022, Meta saw a slight drop (–1.1%) in ad revenue to $113.6B (due in part to economic factors and Apple’s privacy changes), but in 2023 it bounced back to a record $131.95B in ad revenue (+16% YoY)​

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. The introduction of Reels (short video) monetization and improvements in ad targeting via AI helped drive this growth. Meta’s Q1 2024 ad revenue was particularly strong (+26.8% YoY)​

, indicating that advertisers returned to the platform in force, likely attracted by improved performance and possibly higher prices.

In the near term (1–2 years), Meta appears more resilient than Google. The use cases of LLMs (information queries, creative text generation) don’t directly replace scrolling an Instagram feed or watching Facebook videos. Thus, a user might use ChatGPT to get a recipe or answer a question, but they’ll still open Instagram to see friends’ posts or be entertained – and Meta can still serve them ads in that context. In fact, if Google searches decline, some advertising budgets may shift into social media advertising as an alternative, potentially benefiting Meta. For example, a retail brand that cuts back on search ads might increase its spend on Facebook/Instagram ads to reach customers through interest targeting. Meta’s diverse platform family also provides flexibility: if one app’s usage stagnates, another (say, Instagram or WhatsApp) might grow. Instagram has been very strong with younger users (as Facebook usage among youth declined). WhatsApp is just beginning to be monetized (through click-to-message ads and soon possibly in-stream ads in the Status feature), which could unlock new revenue streams especially in regions like MENA, LatAm, and parts of Europe where WhatsApp is ubiquitous.

That said, Meta is not without long-term challenges in an AI-saturated world. A few considerations:

• AI-driven Content and Competition: If generative AI enables the creation of endless content streams tailored to users, the nature of social feeds could change. Meta itself is integrating AI – for instance, they introduced AI chat “Personas” in Messenger and Instagram, and plan to use generative AI to help advertisers create better ads. These could strengthen Meta’s proposition. However, if users start spending time with AI assistants that aren’t on Meta’s platforms (for example, a future AI built into mobile operating systems that curates content or answers questions), that could indirectly reduce time spent on Meta’s apps. There is a concept of “AI as the new social feed” where an assistant might fetch you posts or recommend products without you scrolling a native app – if that materializes, Meta would need to integrate into that loop to keep ad dollars.

• E-commerce and Search Convergence: Meta has tried to capture “intent” with features like Marketplace and Shopping on Instagram. If AI search/chat starts handling product discovery (instead of a user browsing Instagram for inspiration, they might ask an AI for product ideas), Meta could lose some commercial discovery usage. However, Meta’s advantage is the rich personal data and social graph which AI alone lacks; they can still push personalized product ads in feeds that feel serendipitous to users (something an open chatbot can’t easily replicate yet).

• Privacy and Data: Both Google and Meta face the broader issue of user privacy changes (like third-party cookie deprecation, regulations, etc.) which can affect ad targeting. AI doesn’t remove this issue; in fact, Meta has navigated the loss of some data (from Apple’s App Tracking changes) by leaning on AI to better optimize with less data. In the long run, Meta’s diverse data sources (social connections, interests, messages) could be a strength or a regulatory target, but that’s parallel to the AI question.

In the short term, Meta’s platform diversity does provide resilience. Facebook is still huge globally (nearly 3 billion MAUs), Instagram drives cultural trends (and ad dollars in categories like fashion, beauty), and WhatsApp/Messenger have potential as the next ad frontier (especially via business messaging and in-chat bots). Unlike Google, Meta’s usage is not fundamentally about answering questions – it’s about social interaction and entertainment, which means ChatGPT isn’t a substitute. In fact, Meta’s biggest competitive threat was TikTok (a different kind of AI – content recommendation AI – but Meta countered with Reels effectively).

Over a 5-year horizon, Meta will likely face similar long-term questions as all digital platforms: How to integrate generative AI to keep users engaged and not lose them to standalone AI applications. Meta’s strategy seems to be to embed AI agents into its apps (e.g., AI characters users can chat with on Instagram) and to use AI to improve ad performance behind the scenes. If successful, Meta could continue to grow its ad revenues even if the overall digital ad market growth slows. In 2024 Meta is already testing AI-generated ads and automated tools for advertisers, which could particularly help SMEs create ads (aligning with the democratization theme).

To directly address the question: Is Meta more resilient in the short term due to platform diversity? Yes – the evidence shows Meta’s ad business has rebounded and diversified (FB, IG, etc.), and it is not seeing usage declines from AI tools. Does Meta face similar long-term challenges? Potentially yes – if consumer behavior radically shifts to AI-centric engagement (less time on social feeds, more time with AI content curators), Meta would need to adapt its model to ensure its platforms remain central. For now, Meta’s focus on community and entertainment provides a cushion against the immediate AI impact that haunts Google’s search model.

Conclusion

In conclusion, the advent of mass-market AI like ChatGPT is reshaping the digital landscape, but its near-term impact on advertising giants Google and Meta has been incremental, not catastrophic. Google’s search advertising engine is still powering record revenues, yet the growth has slowed, and the company is racing to infuse AI into its products to stay ahead of changing user habits. Meta’s social advertising juggernaut has proven resilient and even accelerated, leveraging its multiple platforms and heavy investment in AI-driven recommendations and ad tools. Across major markets – from the US and Europe to APAC and MENA – advertisers still need to reach consumers, and if one channel becomes less effective, others will rise to fill the gap.

For the digital ad market at large, generative AI presents both a challenge and an opportunity. It challenges incumbents to innovate (as seen by Google’s upcoming Gemini AI search and Meta’s AI features), and it creates opportunities for emerging channels. Video streamers and broadcasters stand out as key potential winners. As we’ve outlined, streaming platforms that adopt digital-like ad models (data-driven, open to all advertisers, flexible formats) can attract ad spend that might otherwise go to Google or stay in traditional TV. The premium TV ad market is poised to be cracked wide open – “democratized” so that not just 500 big advertisers, but thousands or millions, can participate​

. This bodes well for AVOD and hybrid platforms globally, especially as cord-cutting and digital viewing accelerate.

In an executive context for the streaming industry, the message is clear: prepare now. The rise of AI usage means the old truism – that Google and Meta will always soak up virtually all incremental ad dollars – may no longer hold true in a few years. Streamers should invest in ad tech, forge partnerships, and educate advertisers to ensure that when brands reallocate budgets, the streaming ecosystem is ready to capture that demand. Likewise, brands should not pull back on advertising altogether (even if AI changes consumer behavior) – they will simply need to shift where they advertise to follow audiences.

Google and Meta are not disappearing; indeed, they will likely remain dominant players by adapting their strategies (e.g. Google integrating more commerce and transactions into AI search results​

, and Meta blending AI content with social). But the pie of digital advertising is expanding and being redistributed. AI may redistribute attention, and thus revenue, in favor of those who innovate. For streaming services and broadcasters, the door is open to step into a larger role in the digital ads market – one that is more video-centric, AI-augmented, and inclusive of advertisers big and small. The next 1, 2, and 5 years will reveal how this balance plays out, and whether the predictions of search’s decline come to fruition or if incumbents turn AI from threat to tailwind. What’s certain is that all players must stay agile: embracing AI in their own operations while doubling down on the human fundamentals of advertising – reaching the right audience with the right message, wherever they may be.