Is Google Ads on its way out? Why ChatGPT Ads is now reshaping marketing

The end of wasted ad spend? Why context is suddenly everything in ChatGPT Ads

Don't blindly shift budgets: How to successfully test ChatGPT Ads for your business

Since the end of August 2026, OpenAI has been shaking up the digital advertising market: With the introduction of ChatGPT Ads in Germany, a format has emerged that goes far beyond a simple new ad placement. Instead of bidding on isolated keywords as before, companies now have the opportunity to integrate paid reach into deep, context-related dialogues. It's a shift from a simple search query to a complex decision-making situation. The user formulates their problem, prioritizes criteria, and simultaneously receives a perfectly tailored offer.

But does this mean the slow death of industry leader Google Ads? The short answer is: no. The long answer requires a nuanced look at the economic mechanisms of both platforms. While Google remains the machine for capturing existing demand and quick action, ChatGPT positions itself where customers are still seeking advice and pre-qualification. In the following comprehensive analysis, we examine which business models (from e-commerce to B2B) stand to benefit most now, where the limitations of the new platform lie, and why a smart, data-driven test run is currently far more sensible than blind activism.

E-commerce and B2B in transition: For whom advertising in ChatGPT is really worthwhile

Those who continue to bid only on keywords will soon be buying yesterday's attention

Advertising on ChatGPT is more than just an additional ad space alongside Google, Meta, or Amazon. From an economic perspective, it creates a new interface between information search, consultation, comparison, and purchase decision. Since the end of August 2026, users in Germany have also been able to see ads on ChatGPT. These ads initially appear in the Free and Go plans, are marked as sponsored content, and are visually separated from the actual answer. The higher-priced Plus, Pro, Business, Enterprise, and Edu plans remain ad-free. This marks the beginning of an experiment whose significance extends far beyond a new advertising format: For the first time, paid reach can be systematically integrated into a comprehensive dialogue in which the user gradually refines their requirements.

The provocative claim that Google Ads are therefore a thing of the past is nevertheless too simplistic. For the foreseeable future, Google will remain an extremely powerful system for capturing existing demand. The search engine combines enormous reach, established auctions, detailed measurement capabilities, and an advertising infrastructure that has grown over decades. ChatGPT Ads won't replace this structure in the short term. However, they could capture some of the most economically valuable moments before the actual search query: the phase in which a potential customer is still formulating their problem, weighing criteria, trying to understand alternatives, and making a manageable decision from a confusing array of options.

This is where the strategic difference lies. Traditional search advertising primarily reacts to a single entered term. Conversation-based advertising can potentially consider the context of an entire decision-making situation. For companies, this shifts the competitive landscape from who bids the most for a specific keyword to which offer is actually relevant in a concrete usage context. This opens up new opportunities but also increases dependence on a platform whose delivery logic, reach, and pricing are still in an early stage of development.

From search term to decision situation

At its core, Google Ads functions as a marketplace for attention, driven by search terms, target audience characteristics, and predicted responses. Advertisers attempt to understand the intent behind what is typically a short search query. This works relatively well with unambiguous terms like "tax advisor Ulm," "forklift rental," or "CRM software for SMEs." However, with complex search queries, individual keywords inevitably remain incomplete. They reveal what is being searched for, but often not why the search is taking place, what limitations apply, and what objections still need to be overcome.

A chat contains significantly more semantic context. A user might explain that they are looking for software for 40 employees, need to integrate an existing ERP solution, don't have their own IT department, want to process data within the European Union, and want to start within three months. From a marketing perspective, this doesn't create a single signal, but rather a bundle of information encompassing needs, company size, technical environment, risk profile, and time horizon. A relevant ad could therefore theoretically be closer to the actual decision than one that simply responds to "best CRM software.".

This is precisely where the economic promise of ChatGPT Ads lies. Context can reduce wasted ad spend because it takes into account not only the topic but also the situation. At the same time, it can increase the value of a single contact because the user has already completed a significant portion of their research. Someone who clicks on an ad after several follow-up questions is potentially better pre-qualified than someone who clicks on the first paid link after a general search query.

This potential, however, should not be confused with guaranteed precision. OpenAI uses so-called contextual hints related to conversations, topics, and terms when setting up campaigns. These hints are not exact keywords and do not guarantee ad placement in a specific dialogue. This results in a less directly controllable system for advertisers. The platform interprets relevance, while the advertiser only provides guidelines. This can improve the user experience but complicates planning, forecasting, and optimization.

Why the buying process can be shortened

The classic digital customer journey is often fragmented. A user identifies a problem, reads a guide, performs several searches, opens comparison portals, checks reviews, visits manufacturer websites, and later returns via a brand inquiry or a retargeting ad. Every transition creates friction. Information gets lost, devices change, cookies are missing, and some potential customers abandon the process. Marketing departments then try to piece together these scattered interactions into a coherent narrative using attribution models.

An AI-driven dialogue can combine several of these steps into a single interface. ChatGPT first explains a problem, compiles criteria, compares categories, answers follow-up questions, and can ultimately refer to specific providers or offers. When an ad appears at the appropriate point, the gap between orientation and action narrows. The user doesn't have to translate their requirements into a search engine again. A lengthy information journey can be transformed into a shorter transition to the landing page, registration, or inquiry.

This concentration of contacts is particularly valuable for companies when economic success depends not on maximum reach, but on qualified contacts. An industrial software provider doesn't need millions of impressions. A few inquiries with a clear problem definition, a realistic budget, and demonstrable interest in implementation can suffice. The same applies to high-quality services, specialized training, complex travel arrangements, consumer goods requiring explanation, or local offerings with high order values.

However, shortening the buying process has a downside. If AI already handles a large part of the consultation, the number of visits to company websites decreases. Brands lose opportunities to present their product range, their story, and complementary products in detail. Each individual click can become more valuable, while the total number of clicks remains limited. Companies must therefore prepare for a world with fewer users, but those users with more specific expectations. A generic homepage will no longer suffice. The landing page must immediately address the need identified during the conversation and enable the next step without detours.

Reach does not equal market potential

ChatGPT boasts a massive global user base and has evolved from an experiment to an everyday tool for many. However, this overall size shouldn't be confused with the actual ad reach available. Ads only appear in specific plans and usage scenarios. High-volume professional users, executives, consultants, developers, and businesses often work with Plus, Pro, Business, or Enterprise accounts, thus remaining outside the ad inventory. In B2B marketing, this exclusion can be significant.

Even within Free and Go, not every conversation is monetized. Advertising requires a suitable, sufficiently commercial, and brand-safe context. Conversations about personal health, mental health, politics, and other sensitive topics are restricted or ad-free. Minors are also not targeted. Furthermore, users can decline ads, limit personalization, or switch to a free, ad-free version with reduced features. Therefore, the theoretical user base is significantly larger than the practically reachable target audience.

The total number of ChatGPT users is not the deciding factor in evaluating a channel. What matters are the number of conversations that can be reached within a specific category, region, language, and stage of the buying process. A travel provider can expect a relatively large number of commercial dialogues. A manufacturer of highly specialized measurement technology, on the other hand, might only rarely encounter a suitable context, even though each individual contact could be very valuable. Therefore, reach and contact value must be considered together.

In the early market phase, limited inventory can even lead to unusual results. Low competition initially allows for attractive prices, while small ad spend limits budgets and learning progress. Later, increasing demand and a focus on particularly attractive conversational situations can drive up click prices. Therefore, companies should not expect permanently low prices or immediately scalable volumes. ChatGPT Ads are initially more of a learning and options channel than a reliable replacement for established reach-generating tools.

Which business models will benefit first?

This channel is particularly interesting for business models where customers weigh several criteria before making a purchase. These include travel, consumer electronics, household products, furniture, software, further education, local services, and many offerings related to living, leisure, and personal productivity. The greater the need for information and the clearer the differences between the options, the more likely a longer dialogue can create a moment relevant to advertising.

In e-commerce, retailers and brands with well-structured product data, clear specifications, competitive prices, and reliable availability information benefit most. If a user is looking for a lightweight notebook with long battery life, specific ports, and a price limit, the offer must be machine-readable and consistent across the landing page. Incomplete product descriptions, outdated prices, or conflicting variants negate the advantage of precise context. Therefore, this new advertising channel rewards not only creative marketing but also operational data quality.

ChatGPT Ads are also attractive for Software-as-a-Service offerings and digital products. These companies can describe their value propositions, integrations, target audiences, and pricing models relatively precisely. Furthermore, trial registrations, demo requests, and subscriptions can be digitally measured. A contextually relevant contact can lead directly to an industry-specific solution page, a calculator, or a product demo. Providers whose products solve a clearly defined problem and can be purchased or tested without lengthy physical sales processes are particularly promising.

Local service providers gain another interesting option. When a user searches for a moving company, a tradesperson, a course, or a leisure activity, the need, location, and time combine to create a powerful signal. The challenge lies less in the fundamental relevance than in the regional density of the listings. A business can only consistently utilize these listings if enough relevant conversations take place within its catchment area.

In the education and training sector, this channel is well-suited to users who first need to structure their goals. Questions about career changes, AI qualifications, language courses, or technical certifications often develop over several conversational steps. Providers with transparent learning objectives, recognized qualifications, and verifiable results can benefit from this. Exaggerated promises of success, on the other hand, would not only damage trust but also conflict with stricter advertising guidelines.

B2B advertising: between precision and reach gap

For B2B companies, ChatGPT Ads seem ideal at first glance. Industrial procurement, software selection, consulting projects, and outsourcing decisions are complex and information-intensive. A conversation can reveal industry, company size, system landscape, location, regulatory requirements, and budget constraints. Theoretically, this creates a level of needs assessment that traditional keyword campaigns can only achieve with extensive landing pages, forms, and subsequent lead qualification.

In practice, however, the pricing structure limits the potential. Many professional users work on ad-free plans. Those who regularly create extensive analyses, process confidential documents, or use ChatGPT company-wide may not be reached by ads. A B2B provider therefore cannot assume that the most important decision-makers are within the visible inventory. They are more likely to reach freelancers, employees in the early stages of research, smaller companies, and users without a centrally provided corporate plan.

This doesn't make the channel worthless, but it does change its role. ChatGPT ads can generate demand in the B2B sector, reach specialist users, and influence early shortlists. They don't necessarily replace LinkedIn, trade publications, trade shows, partner sales, account-based marketing, or personal outreach. Especially with high-value orders, the actual buying process remains multi-stage and organization-dependent. The ad can generate an initial qualified lead, but it rarely closes the deal.

B2B providers should therefore not focus solely on direct sales. Meaningful goals include demo requests, technical guides, profitability calculators, webinars, or industry-specific case studies. Crucially, the next step must be appropriate to the prospect's stage of development. Trying to force a sales call immediately after an initial exploratory dialogue increases the drop-off rate. Conversely, building on the existing context and offering credible added value can transform early contact into a solid sales opportunity.

Where ChatGPT Ads are unconvincing

Not every business model benefits from conversation-based advertising. Products with very low margins, little differentiation, and high logistics costs may struggle to economically justify additional click costs. If customers primarily choose based on the lowest price, a product search engine or marketplace often remains more efficient. There, price, delivery time, and availability are directly comparable, while a longer dialogue offers little additional value.

This channel can also be unsuitable for extremely spontaneous purchases. Those who already know exactly which spare part, ticket, or consumable product they need usually want to find the answer quickly. A traditional search, a retailer app, or a marketplace shortens this path more effectively than a conversation. ChatGPT ads are more effective where uncertainty needs to be reduced, not where the decision has already been made.

Additional restrictions apply to highly regulated or sensitive categories. Political advertising is not permitted, and ads alongside personal health, mental health, or other vulnerable conversations are prohibited. Financial, medical, and legal providers may be considered depending on their licensing and the context, but they must expect stricter reviews and significantly more limited placements. Particularly in these areas, the commercial intent is often high, while the risk of undue influence is especially significant.

Ultimately, this channel is poorly suited for companies with weak digital foundations. An ad cannot compensate for a slow website, unclear pricing structure, poor mobile usability, or unreliable lead management. On the contrary, the more precise the preceding dialogue, the more noticeable any break in the flow will be on the landing page. Companies should first address their data, websites, conversion tracking, and sales processes before purchasing additional leads.

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The economic advantage lies in the pre-qualification

The value of an advertising channel is often judged by cost per click and conversion rate. This perspective is insufficient for ChatGPT Ads. A higher cost per click can be economically viable if the lead is significantly better qualified, less frequently rejected by sales, and leads to a faster sale. Conversely, a cheap click is worthless if the user is merely curious or perceives the ad as an intrusive addition to their reply.

For a reliable evaluation, companies should consider the total contribution margin per acquired customer. Relevant factors include not only advertising costs, but also sales expenses, cancellation rates, returns, discounts, onboarding costs, and expected customer lifetime value. For B2B offerings, lead quality is also a crucial consideration. If ChatGPT ads generate fewer inquiries, but these more frequently convert into genuine sales opportunities, the channel may justify a higher price.

The most important metric is therefore incremental customer lifetime value. It's crucial to determine how many sales are actually generated as new ones and how many are simply shifted from other channels. A user might have searched for the brand on Google after seeing the ChatGPT ad anyway. Without proper testing, ChatGPT would be credited with success even though the ad only accelerated a purchase that was already certain. This isn't useless, but it's less economically valuable than acquiring a completely new customer.

Companies should also distinguish between short-term efficiency and strategic learning. In an early platform phase, testing can be worthwhile even if the measured return on advertising spend isn't yet superior. The company learns which conversational contexts are relevant, which value propositions work, and which landing pages handle the transition from an AI consultation. This knowledge has option value. It facilitates later scaling once reach, formats, and optimization features mature.

The advertisement competes with a credible answer

In search engines, paid and organic results are displayed side-by-side. Users are familiar with this logic and have learned to either engage with ads or skip them depending on the situation. ChatGPT handles this differently. The actual answer appears as a cohesive consultation. The ad appears separately below it and must compete with the feeling that the user has already received the necessary information. This creates a fundamental tension.

If the response mentions several suitable products or providers, an additional ad can appear redundant. If it mentions other options, a potential conflict arises between organic recommendations and paid placements. Since advertisers cannot influence the response, this relationship cannot be planned. This separation is important for the platform's credibility. However, for businesses, it means that a paid ad does not guarantee inclusion in the actual recommendations.

The creative challenge changes as a result. A ChatGPT ad shouldn't pretend to be the answer. It must offer a meaningful next step. This could be a concrete offer, an availability check, a demo, a calculator, local consultation, or a clear price advantage. General brand messages without immediate benefit are likely to be less effective in a decision-oriented dialogue. The user expects a connection, not an interruption.

This increases the importance of offer architecture. A good ad combines three levels: It recognizes the likely context, formulates a verifiable benefit, and leads to a landing page that delivers precisely that benefit. The traditional separation between advertising material, landing page, and sales remains, but must be more closely coordinated. Those who simply copy existing search ads forfeit the specific advantages of the medium.

Measurability between progress and blind spots

OpenAI Ads Manager now offers campaign goals for viewability, clicks, and conversions. Billing can be based on impressions or valid clicks; conversion-optimized campaigns can be targeted to specific events such as purchase, registration, or lead generation. Reports include impressions, clicks, spend, click-through rate, average cost per click (CPC), cost per thousand impressions (CPM), and conversions. A pixel and a Conversions API are available for technical measurement.

This brings the system closer to the standards of established advertising platforms. Nevertheless, important questions remain unanswered. Advertisers do not receive individual conversation content. This protects privacy but limits diagnostics. A company can see that an ad has been clicked, but does not automatically know the entire thought process that led to that click. The platform possesses more context than the advertiser. This information asymmetry is structural and is likely to persist.

Attribution is also becoming increasingly challenging. A user might see an ad, later visit the brand directly, return via another channel, or complete the purchase offline. Pixels and server-side interfaces improve attribution, but they don't solve every problem. Browser restrictions, consent requirements, device switching, and long sales cycles remain. Furthermore, there's the risk that platform reports and proprietary analytics will produce conflicting results.

Therefore, ChatGPT traffic should be consistently analyzed separately. Dedicated UTM parameters, specific landing pages, CRM tags, and the shared use of pixels and the Conversions API create a better foundation. For longer B2B cycles, marketing data must be linked to sales phases. Only then can it be assessed whether clicks result in qualified leads, quotes, and orders. A high click-through rate alone is not proof of economic success.

Data protection and trust as a production factor

In conversation-based advertising, trust is not a soft image goal, but an economic prerequisite. Users often share more context in chats than in a search bar. Even if advertisers don't have access to conversations, memories, or personal details, the mere impression of an overly precise ad can cause discomfort. The platform must establish relevance without signaling surveillance.

OpenAI attempts to mitigate this conflict through a clear separation of response and display, aggregated reports, control options, and restrictions on sensitive contexts. Furthermore, the European Economic Area has stringent requirements regarding consent, transparency, and data processing. For companies, this means that not only the platform but also their own landing page must operate in compliance with the law. Anyone transmitting conversion data via pixels or interfaces needs a sound data protection basis and must provide users with clear and understandable information.

Trust also extends to content. Exaggerated promises, artificial scarcity, misleading pricing, or purported AI recommendations might generate clicks in the short term, but damage the brand and platform in the long run. The risk of borrowed credibility is particularly high because ads appear alongside answers perceived as helpful. Companies should not exploit this proximity but instead communicate in a way that is easily verifiable.

This is crucial from an economic perspective because the quality of the entire advertising environment determines the future value of the channel. If ads are accepted as relevant and clearly separated, a sustainable market can emerge. If users feel manipulated, they will switch to ad-free plans, reduce their usage, or ignore the ads. Short-term monetization is therefore directly related to the long-term return on trust.

Why Google Ads are far from over

Google's strength lies in its reach, established habits, and immediate proximity to action. Billions of searches generate an enormous variety of commercial intent every day. The search engine remains particularly efficient for local navigation, specific product names, urgent services, and brand-related searches. Furthermore, Google offers sophisticated tools for bidding, audience targeting, product feeds, location tracking, automation, and cross-channel measurement.

ChatGPT, on the other hand, is strong when users haven't yet translated their problem into a precise search term. The systems therefore occupy somewhat different phases. ChatGPT can help define the category and selection criteria. Google can then provide information on prices, retailers, availability, and local options. In many purchasing processes, both platforms are used sequentially or in parallel.

Google is also evolving and integrating generative AI into its search engine. Therefore, the competition is no longer between a static search engine and an innovative chatbot. Both sides are converging: search engines are becoming more conversational, while AI assistants are building advertising auctions, product data, and conversion tracking systems. For advertisers, this doesn't simply mean switching channels, but rather a new distribution of budgets across multiple platforms.

The right strategic question, therefore, is not whether ChatGPT will replace Google. The crucial question is which channel most efficiently reaches which segment of demand. Google Ads remains a robust core channel as long as search volume, marginal costs, and contribution margin are satisfactory. ChatGPT Ads should be tested as a complementary tool that can offer particular advantages in consultation-intensive and still-open decision-making situations. A premature reallocation of large budgets would be just as unwise as waiting completely.

A meaningful test instead of blind activism

A good starting point begins with a clear hypothesis. The company should define the desired conversation scenario, the problem the user is likely trying to solve, and the most economically valuable action. A campaign for general brand awareness requires different content and metrics than a campaign for demo requests or direct purchases. The more precise the hypothesis, the more meaningful the result.

A limited but sufficiently long pilot project should then be set up. The Ads Manager allows a minimum daily budget of €15, but this formal minimum is not automatically enough for statistically reliable results. The budget must be appropriate for the expected click-through rate, conversion rate, and customer lifetime value. For infrequent B2B transactions, a longer duration is required than for frequent e-commerce purchases.

For testing purposes, a few clearly separated use cases are suitable. A software provider, for example, could treat integration needs, cost reduction, and rapid implementation as distinct contexts. Each ad group receives appropriate contextual information, its own value proposition, and a specific landing page. If too many topics are mixed, it remains unclear why a campaign succeeds or fails.

The evaluation should not be limited to the ChatGPT dashboard. Companies need comparative data from Google Ads, organic search, social media, and direct traffic. Key performance indicators (KPIs) such as cost per qualified lead, percentage of accepted sales opportunities, time to conversion, contribution margin, new customer acquisition rate, and repeat purchase value are useful. With sufficient data volume, regional or temporal control groups should be used to differentiate between additional impact and mere traffic shift.

A pilot project is successful if it generates either profitable growth or robust learning. Even if it fails to achieve direct profitability, valuable insights into customer needs, landing pages, and product positioning can still emerge. Crucially, termination criteria must be defined beforehand. Without such boundaries, a strategic test quickly becomes a perpetually subsidized experiment.

The marketing mix needs to be rebalanced

ChatGPT ads shouldn't be planned in isolation. The channel interacts with and is influenced by other touchpoints. A strong brand increases the likelihood that users will trust an ad. Good organic visibility in AI-generated responses can support paid placements. Search ads capture subsequent brand and product queries. Email, retargeting, and sales further nurture the relationship. Economic performance arises from this interplay.

For budget planning, a three-part approach is recommended. The largest share initially remains in established channels with demonstrable marginal returns. A smaller share is allocated to the systematic optimization of existing campaigns and content. A limited innovation allocation finances ChatGPT Ads and other new AI interfaces. This structure protects ongoing business while simultaneously preventing the company from neglecting the development of new competencies.

Alongside paid advertising, unpaid visibility in AI systems must be improved. Companies need clear product information, credible technical content, consistent brand data, transparent pricing, and technically accessible websites. Those who are not easily understood organically will also convert poorly from paid contacts. Generative Engine Optimization is therefore not an alternative to ads, but rather part of the same infrastructure for machine-mediated decision-making.

Coordination with Google is particularly important. If ChatGPT generates new demand, brand-related search queries can increase. If this effect isn't considered, the company might attribute the subsequent conversion solely to Google and underestimate the preceding AI interaction. Conversely, a ChatGPT ad might reach a user who has already been informed via Google. Channel reports must therefore be supplemented with overarching business metrics.

Who controls the new scarcity

Every major advertising platform transforms user attention into a scarce commodity, selling access through auctions. This scarcity is particularly pronounced on ChatGPT because ads cannot be arbitrarily inserted into every conversation without compromising the product's quality. The platform must moderate advertising, exclude sensitive contexts, and simultaneously ensure sufficient relevance. This allows high-quality inventory to remain limited.

OpenAI uses a relevance-weighted second-price auction. The highest bid isn't the only factor; the perceived relevance is also considered. This principle is familiar from other advertising systems, but it takes on additional significance in this context. A provider with a precise offer and a good landing page can theoretically prevail against a larger competitor if their ad appears more relevant to the situation. However, the evaluation of this relevance remains only partially transparent to advertisers.

As demand grows, the platform will be able to leverage its market power. Higher bids, new formats, preferred partnerships, or additional measurement products are all conceivable. Companies should therefore avoid making their customer acquisition dependent on a single AI provider too early. Building their own data, direct customer relationships, strong brands, and recurring revenue remains the most important safeguard against rising platform costs.

This new channel is therefore changing not only advertising, but also the power of negotiation in digital markets. Whoever controls access to the decision-relevant interface can capture a share of the added value. Today, this power lies primarily with search engines, social networks, and marketplaces. AI assistants are emerging as further gatekeepers. Companies gain more choice, but not automatically more independence.

The clear perspective for companies

ChatGPT Ads are neither a revolution that will immediately render Google Ads obsolete, nor an insignificant add-on format. They mark the beginning of an advertising market in which access to customers is determined not only by search terms and demographic profiles, but by ongoing decision-making contexts. The economic potential lies in better pre-qualification, shorter information pathways, and a greater degree of integration between consultation and offer.

The limitations are real. The reachable target audience is smaller than the total ChatGPT user base, professional paid accounts remain ad-free, sensitive topics are restricted, and the ad delivery logic is less transparent than a traditional keyword system. Measurement and conversion optimization are developing rapidly but are not yet at the maturity level of advertising platforms that have been optimized for decades. Companies should expect limited volume, fluctuating results, and a learning curve.

The logical conclusion, therefore, is: test now, but don't blindly shift your focus. Companies with complex offerings, high customer lifetime value, strong product data, and effective landing pages should gain practical experience early on. They can discover which conversational scenarios are economically relevant, how ads integrate seamlessly into a consultative context, and what kind of customer quality emerges from the channel.

Those who simply copy existing search ads, hope for cheap reach, or ignore fundamental measurement problems are likely to be disappointed. Competitive advantage doesn't come from merely booking a new ad space. It comes from the ability to translate context into a relevant offer, a credible promise, and a smooth next step.

Google Ads weren't yesterday's news. What was yesterday was the notion that a single search query could adequately describe all customer needs. The future of customer acquisition belongs to marketing that integrates search, dialogue, data, and sales into a cohesive decision-making process. ChatGPT Ads are an early, still imperfect, but strategically relevant building block of this development.

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