What Gravity Actually Built
Gravity raised $30.5 million in Series A funding, bringing total funding to $38.5 million, to build advertising infrastructure specifically for AI environments. The platform serves two distinct use cases.
The first is conventional AI placement: ads appearing within chatbot interfaces and AI-powered search results, similar to how sponsored results appear in traditional search.
The second is more significant: agent-to-agent advertising, in which advertiser catalogue data, product attributes and commercial offers can be supplied directly to AI agents that are evaluating what to recommend or purchase on behalf of a user.
Gravity says customers have included Best Buy and Target. The company ultimately wants agent interactions to extend through recommendation and payment completion. These are early-stage commercial products, and there is not yet independent evidence showing incremental ROI at large scale.
The Four Advertising Commodities
Traditional advertising has monetised three commodities:
Attention (social, display, video): The advertiser pays to be seen by a human audience.
Declared intent (search): The advertiser pays to appear when a human expresses a specific need.
Transaction context (retail media): The advertiser pays to appear at the point of purchase decision.
Agent advertising introduces a fourth commodity:
Machine decision influence: The advertiser pays to supply commercial signals directly to the software acting for the buyer, potentially before the buyer ever sees a conventional ad.
That is a qualitatively different value proposition. The human impression may be eliminated entirely. The commercial influence is preserved - or potentially amplified, because the agent acts on the signal rather than merely seeing it.
Why This Changes Everything About Media Planning
If an AI agent evaluating CRM software for a 200-person B2B company can receive a commercial signal from Salesforce before it generates its recommendation, the entire advertising funnel changes:
- Media planning shifts from audience targeting to agent targeting: which AI systems are making decisions in your category?
- Product feeds become commercial intelligence packages: structured data that agents can parse and act on, not just display ads.
- Attribution becomes impossible with conventional last-click or even multi-touch models: the agent's decision may have been influenced by a signal consumed hours or days before the human saw any output.
- Disclosure becomes a new regulatory and ethical challenge: how does a consumer know that the AI recommendation they received was influenced by a paid commercial signal?
- GEO/AEO strategy intersects directly with agent advertising: being cited organically by AI systems and being commercially present in agent decision layers are now related but distinct strategies.
The Disclosure Problem
Search engines solved the paid-versus-organic distinction imperfectly but visibly: "Sponsored" labels on paid results, separated from organic rankings.
Agent-to-agent systems need a new transparency model. When an AI agent recommends Brand A partly because Brand A paid to enter its commercial decision layer, how does the consumer know?
The challenge is that the recommendation may be delivered as a natural language statement - "Based on your requirements, I would suggest Brand A" - with no visual distinction between paid and organic influence.
This is not a hypothetical future problem. Gravity's platform is live with enterprise customers today. The disclosure standards do not yet exist.
What B2B Marketers Should Do Now
Agent advertising is early-stage. But the strategic direction is clear enough to begin preparing:
Audit your product data: Agent advertising systems consume structured catalogue data. Brands with clean, comprehensive, machine-readable product and service data will be better positioned to participate in agent advertising platforms.
Separate organic and paid AI visibility: Track your brand's organic citation rate in AI systems (GEO/AEO) separately from any paid agent advertising. These are different channels with different economics and different trust signals.
Watch the disclosure landscape: Regulatory attention to AI advertising disclosure is increasing. Brands that establish clear internal policies now will be better prepared when external standards emerge.
Consider the attribution implications: If agent advertising influences decisions that are later attributed to other channels (direct, organic, referral), your measurement stack will systematically misattribute the value. Plan for this now.
Modi Elnadi is the founder of Integrated.Social, a B2B AI marketing agency in London specialising in agentic AI lead generation, AEO/GEO and performance marketing.
