Just for a minute, imagine a world where you, as a consumer, never see an ad again — at least not directly.

Sounds too good to be true, right?

But this might soon become reality, thanks to a concept proposed by Aravind Srinivas, CEO of Perplexity AI.

The idea is simple yet revolutionary: instead of targeting human attention, digital advertisements will target AI agents — the virtual assistants that increasingly manage our lives.

This shift could fundamentally change how digital advertising operates.

Instead of vying for your clicks or screen time, brands will compete for the attention of your AI assistant, which acts as an intermediary between you and the digital marketplace.

It’s a bold vision, one that challenges decades of advertising norms and opens up new possibilities for privacy, personalization, and efficiency.

• But how does this work?
• Why does it matter?
• And what are the implications — for businesses, consumers, and the advertising industry as a whole?

That’s what’s been on my mind for the last 24 hours, and I want to discuss it today.

Let’s get started.

What Are AI Agents and Why Are They Important?

Unlike traditional AI tools or chatbots that merely respond to user prompts, AI agents are proactive, autonomous systems designed to perform complex tasks on behalf of users.

They can reason, plan, and execute actions across multiple platforms, making them indispensable in an increasingly interconnected world.

But why are they important, and how do they fit into the future of digital advertising?

The Evolution from Chatbots to AI Agents

To understand the significance of AI agents, it helps to look at their evolution.

Traditional chatbots were designed to handle specific queries — think of them as reactive tools. You ask a question, and they provide an answer.

While useful, their functionality was limited by their lack of context retention and inability to perform multi-step tasks.

AI agents, on the other hand, are a leap forward in capability.

They don’t just answer questions; they solve problems. For example, an AI agent can create a personalized travel itinerary, book flights and accommodations, sync everything with your calendar, and even adjust plans dynamically based on real-time changes like weather or flight delays.

This level of autonomy makes them far more versatile than their predecessors.

In the context of marketing and advertising, this means that AI agents can analyze vast amounts of consumer data, predict behaviors, and make decisions that align with both user preferences and business goals — all without requiring constant human input.

Why AI Agents Are Poised to Dominate Digital Advertising

The rise of AI agents is not happening in isolation; it’s part of a broader trend toward automation and personalization in the digital economy.

Here’s why they’re becoming crucial in advertising:

1. Personalized Decision-Making: AI agents can sift through enormous datasets — social media activity, browsing history, purchase patterns — and use predictive analytics to tailor recommendations or ads that align perfectly with individual preferences. This level of personalization is something traditional advertising methods struggle to achieve.
2. Autonomy in Action: Unlike static algorithms that require manual adjustments, AI agents operate autonomously. For instance, an agent could dynamically adjust ad campaigns based on real-time user engagement metrics or market trends. This reduces inefficiencies and ensures that marketing efforts are always optimized for maximum impact.
3. Consumer-Centric Approach: These days where privacy concerns are growing louder, targeting AI agents rather than humans directly could be a huge change. Instead of bombarding users with ads, brands can provide value by engaging with their AI assistants — essentially outsourcing decision-making to a trusted intermediary.
4. Scalability: Managing millions of individual consumer interactions manually is impossible for any brand. AI agents make this scalable by automating customer segmentation, content delivery, and even customer support across multiple channels.

The Role of Context Retention

One of the standout features of modern AI agents is their ability to retain context over time. This means they can understand not just what you want right now but also anticipate future needs based on historical data.

For example:

• If you frequently shop for groceries online every Sunday evening, your AI agent might preemptively suggest deals or even place an order for your usual items.
• In advertising, this could translate into hyper-personalized campaigns where the agent knows when you’re most likely to engage with certain types of content or offers.

This ability to “think ahead” makes AI agents uniquely valuable in creating seamless consumer experiences.

Why Now? The Timing Behind the Rise of AI Agents

The sudden surge in interest around AI agents isn’t coincidental — it’s driven by several converging factors:

• Advancements in Machine Learning: Modern machine learning/ large-language models (LLMs) are more capable than ever at handling complex tasks like natural language understanding and decision-making.
• Edge Computing: With Edge AI enabling real-time processing on devices rather than relying solely on cloud infrastructure, latency issues are minimized while privacy is enhanced.
• Consumer Expectations: Today’s consumers demand convenience and personalization at every touchpoint. AI agents meet these expectations by acting as intermediaries that simplify decision-making processes.
• Economic Pressures: Businesses are under constant pressure to do more with less — whether it’s cutting costs or scaling operations without proportional increases in resources. AI agents offer a cost-effective way to achieve these goals.

The Bigger Picture

At its core, the rise of AI agents signifies a shift from reactive technology to proactive systems that enhance human capabilities rather than merely replicating them.

In digital advertising specifically, this means moving away from intrusive tactics like pop-ups or banner ads toward value-driven interactions mediated by intelligent systems.

As we move deeper into 2025 and beyond, it’s clear that businesses ignoring this trend do so at their peril.

The question isn’t whether AI agents will become integral to digital marketing — it’s how quickly brands can adapt to leverage their full potential.

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How AI-Targeted Advertising Works

The concept of targeting AI agents instead of humans in digital advertising may sound futuristic, but it’s already gaining traction as a transformative approach.

At its core, this model shifts the focus from directly influencing human attention to engaging the AI agents that act as intermediaries for users.

These agents — whether they’re virtual assistants like future AI-powered Siri, Alexa, or more advanced personal AI systems — are becoming gatekeepers of consumer decisions.

But how does this new advertising model actually work?

Bidding for AI Attention: A New Marketplace

In traditional digital advertising, companies bid for ad placements on platforms like Google or Facebook to reach human users directly. This involves targeting specific demographics, behaviors, or interests to maximize engagement.

With AI-targeted advertising, the bidding process shifts toward capturing the attention of AI agents.

Here’s how it works:

1. Understanding User Preferences: AI agents are programmed to prioritize user preferences and needs. They analyze historical data — such as purchase history, browsing behavior, and even real-time context — to understand what their users want.
2. Ad Auctions for AI Agents: Businesses bid to have their ads considered by these AI agents. Similar to programmatic advertising today, companies compete for keywords or categories that align with potential user queries. For example, if a user’s AI agent is tasked with finding a new smartphone, advertisers like Apple or Samsung might bid for the agent’s attention.
3. Filtering and Prioritization: Once bids are placed, the AI agent evaluates them based on predefined algorithms tuned to user preferences. This means that only the most relevant and high-quality ads are presented to the user — or in some cases, acted upon directly by the agent (e.g., making a purchase recommendation).
4. Seamless Integration: Unlike intrusive banner ads or pop-ups, these advertisements are seamlessly integrated into the responses provided by the AI agent. For instance, when you ask your assistant for restaurant recommendations, it might include a sponsored suggestion that aligns with your dining preferences.

This marketing model not only streamlines the advertising process but also ensures that ads are more relevant and less disruptive.

The Role of User Preferences in Ad Filtering

One of the most intriguing aspects of this approach is how heavily it relies on user preferences.

Unlike traditional ads that often feel generic or irrelevant, AI-targeted advertising tailors its content to individual needs through advanced filtering mechanisms.

• Preference-Based Algorithms: AI agents use machine learning algorithms to continuously refine their understanding of user preferences. For instance, if you frequently shop online for sustainable products, your agent will prioritize ads from eco-friendly brands.
• Contextual Relevance: The timing and context of ad delivery are critical. If your agent knows you’re planning a vacation, it might prioritize travel-related ads during your search for accommodations or flights.
• Dynamic Adaptation: As user preferences evolve over time, so do the filtering criteria of the AI agent. This ensures that ads remain relevant even as consumer habits change.

This hyper-personalized approach benefits both users and advertisers.

Users get recommendations that genuinely align with their interests, while businesses achieve higher engagement rates and better ROI.

AI Agents as Gatekeepers

In this new paradigm, AI agents essentially become gatekeepers between advertisers and consumers.

This dynamic introduces several key changes:

1. Reduced Cognitive Load on Users: Instead of being bombarded with countless ads across platforms, users rely on their AI agents to curate and filter options on their behalf.
2. Trust-Based Interactions: Users tend to trust their personal AI systems more than traditional advertising channels because these agents are designed to act in their best interest.
3. Shift in Power Dynamics: The power shifts from advertisers controlling ad exposure to users (via their AI agents) controlling what they see and engage with.

For advertisers, this means that success hinges not just on crafting compelling messages but also on aligning those messages with user-centric algorithms.

The Technology Behind It

The mechanics of AI-targeted advertising rely on several advanced technologies:

• Natural Language Processing (NLP): Enables AI agents to understand complex user queries and match them with relevant ad content.
• Machine Learning Models: Continuously improve ad filtering processes by learning from user interactions and feedback.
• Predictive Analytics: Anticipate future user needs based on historical data trends.
• Real-Time Bidding Systems: Allow advertisers to compete for ad placements dynamically as users make requests through their AI agents.

These technologies work together to create an ecosystem where ads are not just targeted but also deeply personalized and contextually relevant.

A Win-Win Scenario?

At first glance, this model appears to be a win-win scenario:

• For users, it reduces ad fatigue by ensuring they only encounter relevant suggestions.
• For businesses, it increases efficiency by targeting high-intent consumers through their trusted intermediaries (AI agents).
• For platforms hosting these interactions (like Perplexity or ChatGPT), it opens up new revenue streams without compromising user experience.

However, as promising as this sounds, there are challenges — both technical and ethical — that need addressing before this model can fully take off.

I want to discuss those in greater detail in later sections.

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Challenges and Ethical Considerations in AI-Targeted Advertising

As exciting as the concept of AI-targeted advertising is, it comes with its own set of challenges and ethical dilemmas.

The idea of targeting AI agents instead of humans may seem like a more efficient and user-friendly approach, but it also introduces complexities that cannot be ignored.

These challenges range from technical hurdles to broader societal concerns about fairness, transparency, and consumer autonomy.

Let’s unpack these issues to understand what’s at stake.

Transparency and Accountability: The Black Box Problem

One of the most significant challenges in AI-targeted advertising is the lack of transparency in how AI systems operate.

Unlike traditional advertising channels where decision-making processes are relatively straightforward, AI agents rely on complex algorithms to evaluate and filter ads.

This creates what is often referred to as the “black box” problem — users and even advertisers may not fully understand how decisions are made.

• Opaque Decision-Making: AI agents evaluate bids for ad placement based on algorithms that prioritize relevance, user preferences, and other factors. However, these algorithms are rarely transparent, leaving users in the dark about why certain ads are shown while others are filtered out.
• Trust Erosion: A lack of transparency can erode trust between users and their AI agents. If users suspect that their agents are prioritizing ads based on factors like higher bids rather than genuine relevance, they may lose faith in the system.
• Accountability Issues: When something goes wrong — such as biased ad delivery or irrelevant recommendations — it’s often unclear who is accountable: the advertiser, the platform hosting the AI agent, or the developers of the agent itself.

To address these issues, businesses must prioritize algorithmic transparency.

This could involve providing users with clear explanations of how their AI agents evaluate ads and offering mechanisms for feedback or dispute resolution.

Algorithmic Bias: A Threat to Fairness

AI systems are only as good as the data they are trained on.

If this data contains biases — whether related to gender, race, socioeconomic status, or other factors — those biases can be perpetuated by the AI agent.

In advertising, this can lead to discriminatory outcomes that harm both consumers and smaller businesses.

Examples of Bias:

• Job ads disproportionately targeting specific demographics (e.g., male-dominated industries being advertised primarily to men).
• Financial services ads being shown more frequently to wealthier individuals while excluding lower-income groups.

Impact on Smaller Businesses:

Larger companies with bigger budgets may dominate ad auctions, making it difficult for smaller businesses to compete for AI agent attention.

This could create a marketplace where innovation and diversity are stifled.

To mitigate these risks, companies must regularly audit their AI algorithms for bias. Training models on diverse datasets and implementing fairness checks can help ensure that ad delivery is equitable across different user groups.

Manipulation Risks: Who Controls Consumer Choices?

Another ethical concern is the potential for manipulation.

By targeting AI agents instead of humans directly, advertisers gain significant influence over consumer choices — perhaps more than ever before.

• Loss of Autonomy: If an AI agent consistently prioritizes certain brands or products based on advertiser bids rather than user preferences, it could subtly shape consumer behavior without their explicit awareness.
• Homogenized Experiences: Over time, reliance on AI agents could lead to a narrowing of choices for consumers. Lesser-known brands or niche products may struggle to gain visibility if they cannot compete financially with larger players.
• Power Imbalances: The concentration of decision-making power in AI agents raises questions about who ultimately controls the flow of information in digital marketplaces — the user or the advertiser?

To counteract these risks, I hope businesses will design AI systems that prioritize user interests above all else.

I think providing users with greater control over their agent’s decision-making criteria can help ensure that their autonomy is preserved.

Technical Hurdles: Building Reliable Systems

Beyond ethical concerns, there are also significant technical challenges associated with implementing AI-targeted advertising at scale:

1. Data Quality: Poor-quality or incomplete data can lead to inaccurate ad targeting, undermining the effectiveness of campaigns.
2. Infrastructure Requirements: Processing large volumes of real-time data requires scalable IT infrastructure — a barrier for smaller businesses looking to adopt this model.
3. System Errors: Even minor bugs in an AI agent’s algorithm can result in flawed ad recommendations or biased filtering processes.

Investing in robust testing protocols and scalable cloud-based infrastructure might help overcome these hurdles.

The Need for Industry Standards

Given the complexities involved in AI-targeted advertising, industry-wide standards are essential for ensuring fairness, transparency, and accountability.

These standards could include:

• Regular audits of AI algorithms by independent third parties.
• Clear guidelines on acceptable data collection practices.
• Mechanisms for users to report issues or provide feedback on their agent’s performance.

Collaboration between businesses, regulators, and consumer advocacy groups will be key to establishing these standards.

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Economic Implications for Brands and Platforms

I am certain that by targeting AI agents instead of human users, brands and platforms are poised to unlock new revenue streams, optimize resource allocation, and disrupt traditional advertising models.

However, this transformation also demands significant strategic adjustments for businesses across the board.

A New Revenue Model for Platforms

One of the most immediate economic impacts of AI-agent-targeted advertising is the creation of a new revenue model for platforms that host these interactions.

Platforms like Perplexity AI, ChatGPT, and others stand to benefit significantly by monetizing the attention of AI agents rather than directly targeting users.

Bidding Wars for AI Attention

In this model, advertisers bid to have their content prioritized by AI agents. This creates a competitive marketplace where platforms can charge premium rates for ad placements that align closely with user preferences.

For example:

• A travel company might bid for an AI agent’s attention when a user searches for vacation options.
• A financial services provider could pay to have its loan products recommended during a user’s search for mortgage options.

Subscription-Free Models

By generating significant ad revenue through AI-agent targeting, platforms may be able to offer more free or low-cost services to users.

This could democratize access to advanced AI tools while maintaining profitability.

Diversification of Revenue Streams

Platforms that previously relied on subscription models or traditional ad placements can now diversify their income by tapping into this emerging advertising framework.

Cost Efficiency for Advertisers

For advertisers, targeting AI agents offers a more cost-effective way to reach high-intent consumers.

Traditional digital advertising often involves significant waste — ads are shown to users who may have little interest in the product or service being promoted.

By contrast, AI-agent targeting allows brands to focus their budgets on consumers who are actively seeking relevant solutions.

1. Reduced Ad Waste: Since AI agents filter ads based on user preferences and needs, advertisers can avoid spending on irrelevant impressions or clicks. This precision targeting minimizes wasted budget and maximizes ROI.
2. Higher Conversion Rates: Ads delivered through AI agents are more likely to resonate with users because they align with explicit intent (e.g., a user asking their agent for product recommendations). This leads to higher engagement and conversion rates.
3. Dynamic Budget Allocation: Advertisers can use real-time bidding systems to allocate budgets dynamically based on campaign performance metrics. For instance:

• If an ad performs well with one segment of users, budgets can be reallocated to target similar audiences.
• Conversely, underperforming ads can be paused or adjusted without incurring additional costs.

This level of efficiency is particularly valuable in industries with tight margins or high competition.

Economic Disruption in Traditional Advertising Models

The rise of AI-agent-targeted advertising represents a fundamental disruption to traditional advertising models.

Companies that fail to adapt risk losing relevance in an increasingly automated and personalized marketplace.

Shift in Budget Allocation

Brands will need to reallocate their marketing budgets away from broad-based campaigns toward strategies that optimize engagement with AI agents.

This may involve:

• Investing in data analytics and machine learning tools to understand how AI agents evaluate ads.
• Developing creative assets specifically designed to appeal to algorithmic decision-makers rather than human emotions.

Impact on Ad Agencies

Traditional ad agencies may struggle to remain competitive unless they embrace AI-driven strategies.

Agencies will need to pivot from designing campaigns aimed at human audiences to crafting content optimized for AI algorithms.

Market Consolidation Risks

Larger companies with greater resources may dominate bidding wars for AI attention, potentially marginalizing smaller businesses.

This could lead to increased market consolidation unless safeguards are implemented to ensure fair competition.

Boosting Innovation in AdTech

The economic implications extend beyond advertisers and platforms.

The rise of AI-agent-targeted advertising is likely to fuel innovation in the AdTech sector as well.

Development of New Tools

Companies specializing in AdTech will develop new tools and frameworks designed specifically for this emerging model. These might include:

• Advanced bidding algorithms tailored for AI-agent marketplaces.
• Analytics platforms that provide insights into how ads perform within agent-mediated ecosystems.

Growth in Related Industries

The demand for expertise in machine learning, natural language processing (NLP), and predictive analytics will create opportunities in adjacent industries like cloud computing and data science.

Job Creation in High-Skill Roles

While automation may reduce demand for certain roles (e.g., manual ad placement), it will likely create new opportunities in areas like algorithm design, data analysis, and campaign optimization.

This wave of innovation has the potential to redefine how brands approach digital marketing while driving economic growth across multiple sectors.

Challenges in Economic Equity

While the economic benefits are clear, there are also risks associated with equity and accessibility:

• Barriers for Small Businesses: Smaller companies may struggle to compete against larger brands with deeper pockets in bidding wars for AI attention. This could exacerbate existing inequalities in market visibility.
• Cost of Entry: Developing campaigns tailored for AI agents requires investments in technology and expertise that may be prohibitive for startups or small enterprises.
• Potential Monopolization: If a few dominant platforms control access to AI agents, they could wield disproportionate influence over the digital advertising ecosystem — potentially stifling competition and innovation.

I believe that to address these challenges, policymakers and industry leaders must explore measures such as tiered pricing models or subsidies for smaller players.

My Final Thoughts: AI Agents Will Be The New Brand Ambassadors

In the near future, AI agents themselves may take on the role of brand ambassadors. These agents won’t just serve as intermediaries between consumers and advertisers — they’ll actively represent brands in digital ecosystems.

We’re standing at the edge of a seismic shift in digital marketing.

So, after all these thoughts,,,I came to the conclusion that…the future of advertising isn’t about chasing human attention anymore; it’s about earning the trust of the AI agents that guide it.

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