Amazon, social platforms and major cloud players are committing over 600 billion euros to accelerate artificial intelligence: data centers, chips, models, but also new uses for content. Behind the hype, the balance of power is already shifting in advertising, e-commerce and influence.
This wave of investment is not simply a technological gamble, but a conquest strategy where each giant seeks to lock down its value chain. Between infrastructure, distribution, and social interfaces, AI is becoming the invisible layer that arbitrates attention, average spend, and the credibility of creators.
To understand this shift, three aspects must be considered: economic drivers, new visibility mechanisms, and concrete implications for brands. The signs are everywhere, from augmented reality design studios to assistant-driven purchasing processes.
Amazon and AI: Why the infrastructure is worth hundreds of billions
In the race for AI, the decisive advantage rarely lies in a spectacular demo. It lies in the ability to produce, train, and serve models at scale, without service disruption. Amazon is at the heart of this movement, because modern AI consumes three scarce resources: energy, computing power and usable data.
The common thread is illustrated by the case of “Maison Lysa,” a fictional cosmetics brand that sells both on marketplaces and via social commerce. When this brand launches a campaign, AI is involved at every stage: demand forecasting, asset generation, ad targeting, customer service, and product recommendations. If even one component falters, costs skyrocket and performance plummets.
Data centers, chips and operational sovereignty
Investing “more than 600 billion” means first and foremost securing access to GPUs and specialized architectures, while simultaneously increasing the density of data centers. This logic is reminiscent of the electrical revolution of the early 20th century: those who controlled the networks controlled the industry. Today, those who control computing control the iteration speed of AI products.
For a brand like Maison Lysa, the consequence is tangible: AI can evolve from a "friendly" assistant to a management system that reduces stockouts, adapts promotions in real time, and optimizes product pages. This optimization depends on the cloud, but also on a trade-off: internalizing certain models or using ready-made APIs.
The reading chart: where the money goes, and why that changes marketing
To clarify, here is a grid that links investment items to observable marketing impacts.
| AI Investment Position | Objective of the web giants | Concrete effect for a social media-oriented brand |
|---|---|---|
| Data centers | Increase training and inference capacity | Faster asset creation, large-scale customization, stabilized costs |
| Chips and accelerators | Reduce dependence on suppliers and improve efficiency | Less latency on recommendations and ad targeting |
| Fundamental models | Create proprietary AI platforms | More powerful generation tools, but risk of technological lock-in |
| Integrated AI products | Capturing everyday use (research, shopping, creation) | New formats, more complex attribution, need for "machine-readable" content |
| Data & Content Partnerships | Improve the quality of responses and recommendations | Increased strength of trust signals: reviews, user-generated content, creators |
The key point: this spending is less about “making AI” than about becoming AI on which others rely. And when the infrastructure is consolidated, the next battle naturally focuses on the interfaces of attention: social networks.
This shift sets the stage for the next topic: how social platforms transform these models into new usage reflexes, and why influence is at the forefront.
When artificial intelligence reshapes influence and social networks
Massive investments only make sense to the public when they translate into tangible results. Social media is where AI becomes visible: more "precise" recommendations, assisted content creation, dubbing, filters, conversational shopping. The consequence is direct: influence is no longer limited to an audience; it becomes a production and adaptation capacity continuously.
Maison Lysa, once again, observes a common phenomenon: "average" content can outperform if it's enriched with signals that the algorithm understands better (descriptions, intent, visual consistency, quick DM replies). Why? Because models now classify posts as semantic objects, not just as videos.
Augmented creation: accelerating without diluting authenticity
The temptation with AI is to produce more. The risk, however, is homogenization. The methodical approach is to reserve automation for what doesn't bear a distinctive signature: format derivatives, hook variations, subtitling, and audience-specific adaptations. For everything else, human oversight remains, particularly regarding the approach and evidence (testing, behind-the-scenes work, feedback).
Platforms like Meta are pushing this logic at high speed. To keep up with these developments, a useful detour is through AI uses that are already transforming social networksbecause they determine how an influence campaign should be briefed and measured.
Effects on discovery: from virality to “findability”
With AI, discovery is becoming more like a multimodal search engine: text, image, intent. This shifts the strategy: it's no longer enough to be entertaining, you have to be findableWinning brands align wording, visuals and usage context, then let the algorithm do its work.
As such, visual research is progressing rapidly. A striking example can be found in the rise of AI-powered visual search, which pushes brands to think of their content as “living catalogues”, where every detail can trigger a recommendation.
This new balance raises a pragmatic question: how to protect trust, manage risks (deepfakes, saturation), and transform these tools into a measurable competitive advantage? This is the operational challenge of the following section.
Execution strategies for brands: capturing the value of the $600 billion without being controlled by the algorithm
When tech giants invest hundreds of billions, the temptation is to believe that “everything will be automated.” In reality, the advantage shifts to those who effectively orchestrate resources: data, creators, platforms, formats, and social proof. AI becomes a lever, not an autopilot. The most robust method is to establish a system where each tool serves a clear marketing purpose.
Maison Lysa has implemented a simple protocol: a library of validated messages (benefits, objections, evidence), creative templates, and then controlled testing on three platforms. AI is used to accelerate ideation and development, but human validation remains non-negotiable when it comes to product promises or compliance.
Measurement and attribution: avoiding false positives
The more AI personalizes, the harder attribution becomes. A post can influence a purchase two days later through a search, a recommendation, and then an item added to a cart on another platform. The operational response relies on hybrid metrics: incremental performance, creator codes, panels, and qualitative analysis of comments. A brand that simply "follows ROAS" risks cutting off what fuels demand.
To frame this topic, it is useful to link influence and AI to a 2025-2026 vision of the impacts, as detailed in Analysis of the impacts of artificial intelligence on influencer marketingThis type of framework avoids confusing automation and strategy.
Authenticity, compliance, and the rise of synthetic content
Generative creation opens the door to risks: false testimonials, undeclared avatars, misleading edits. The key principle is that anything related to trust must remain explainable. A strong brand documents its collaborations, secures rights, and clarifies augmented content.
In this context, understanding the mechanics of artificial intelligence applied to content creation It helps decide what to automate and what to protect. The gain isn't just speed: it's multi-channel consistency, essential for being recognized by recommendation systems.
Final insight: the advantage lies with the orchestrators
The best way to interpret the $600 billion figure is to see it as a shift in gravity: AI makes execution cheaper, but makes differentiation more demanding. It's not the brands that "use tools" that win, but those that build a verifiable narrative and then scale it up without compromising it.
To transform these dynamics into results, ValueYourNetwork provides a concrete framework and proven execution. Expert in influencer marketing since 2016the network relies on hundreds of successful campaigns on social networks and recognized expertise in connecting influencers and brands with the right strategy, the right formats, and the right indicators. To structure a truly high-performing and secure AI approach, contact us.