Chalice AI and Equativ advance containerized AI curation and activation across the open internet. Chalice AI, the platform-independent AI media decisioning company, announced a partnership with Equativ, the leading independent media platform, to bring containerized AI decisioning into Equativ's curation and activation platform. The collaboration expands adoption of the IAB Tech Lab's Agentic Real-Time Framework (ARTF) and advances the use of portable AI models across programmatic workflows. As AI moves from experimentation into production, advertisers and agencies are seeking systems that can operate across platforms without being locked into a single environment. This partnership enables Chalice's custom AI models to be deployed as containerized agents within Equativ's infrastructure, allowing decisioning and execution to operate together within standardized, interoperable frameworks. Through this integration, media buyers can activate Chalice's outcome-driven AI bidding models directly within the Maestro by Equativ media platform, allowing them to score and select impressions in real time based on predicted performance, dynamically adjust bid pricing, and continuously optimize toward specific business outcomes such as conversions or cost per action. "Equativ's adoption of sellside decisioning under ARTF marks exactly the kind of forward-looking partnership we built Chalice to enable," said Adam Heimlich, CEO of Chalice AI. "When a supply-side platform deploys AI within the auction environment using an open, standardized framework, it creates real value for advertisers - not just faster decisioning, but smarter, more accountable outcomes. This is what the unbundling of programmatic looks like in practice." Curation has emerged as a critical control point in programmatic advertising. By introducing containerized AI into Equativ's environment, Chalice enables its proprietary dynamic, impression-level decisioning aligned to advertiser-defined outcomes. By integrating Chalice's engine directly into the supply layer, optimization decisions can be made with high signal fidelity and at the impression level. This approach allows curation and activation to function as a unified system, improving both transparency and performance. "Containerized AI curation fundamentally changes where intelligence sits in the supply chain," said Curt Larson, Chief Innovation Officer at Equativ. "By moving intelligence closer to the supply through curation, buyers gain control and access to the granular, page-level signals that truly matter: how ads are laid out, how audiences actually engage, and what the environment really looks and feels like." The partnership builds on Chalice and Equativ's work advancing containerized AI within programmatic infrastructure and supporting emerging standards like ARTF, which enable AI agents to operate directly within live media transactions. By deploying AI models as portable containers, Chalice and Equativ are enabling a more open and interoperable ecosystem where decisioning can move seamlessly across platforms.

Genius Sports (GENI) hits 52-week low following 50% year-to-date decline. Published on April 12, 2026 at 5:52 pm by faheem tahir in news. Genius Sports Limited (NYSE:GENI) secures a spot on our list of the best penny stocks set to explode. As of April 8, 2026, 89% of covering analysts remain bullish toward Genius Sports Limited (NYSE:GENI), with the consensus price target of $11.50 implying a 171.87% upside. As of April 7, 2026, the stock has declined by over 43% in the past year, now closing at a new 52-week low of $4.18. The stock is down over 60% year-to-date. Earlier, on April 2, 2026, Citi reduced its price target for Genius Sports Limited (NYSE:GENI) from $11 to $9 and assigned a "Buy" rating to the stock. The revision appeared amid the company's continuous expansion of its ad-tech ambitions. Meanwhile, on March 27, 2026, Genius Sports Limited (NYSE:GENI) announced fresh partnerships with DirecTV Advertising, Equativ, FreeWheel, Index Exchange, Magnite, OpenX, PubMatic, and The Weather Company. These partnerships will enable integration of Moment Engine across partners that collectively represent 90% of the programmatic advertising ecosystem. This development came as the company sought to expand distribution for its Moment Engine platform. The platform has been designed to target advertisements around particular in-game moments in real time using live sports and fan-engagement data. According to Genius Sports Limited (NYSE:GENI), this platform is currently accessible on connected TV, online video, and display, with plans to expand to Meta platforms in Q2. Genius Sports Limited (NYSE:GENI), based in London, U.K., and founded in 2000, offers scalable technology solutions for media, sports, and sports betting. These products include platforms for fan interaction, live data, odds management, and risk services. While we acknowledge the risk and potential of GENI as an investment, our conviction lies in the belief that some AI stocks hold greater promise for delivering higher returns and doing so within a shorter time frame. If you are looking for an AI stock that is more promising than GENI and that has 10,000% upside potential, check out our report about this cheapest AI stock.

AdCP & the rise of Agentic Media Buying. Scope3's Ad Context Protocol enables AI agents to negotiate, buy, and optimise advertising without traditional DSP/SSP infrastructure. With Amazon DSP, Magnite, and Index Exchange as launch partners, agentic media buying is going live. Founder, Advertising Protocols March 14, 2026 On March 13, 2026, Scope3 launched its Agentic Media Platform with Amazon DSP as the first integration partner, alongside Index Exchange, Equativ, and media.net as launch distribution partners. This was not a concept demo - it was a production deployment of agent-to-agent advertising transactions using the Ad Context Protocol (AdCP). AdCP, first proposed by Scope3 in October 2025, is an open protocol for agent-to-agent advertising transactions built on Anthropic's Model Context Protocol (MCP). Where traditional programmatic uses OpenRTB bid requests flowing between deterministic software systems, AdCP enables AI buyer and seller agents to negotiate ad placements using natural language intent, structured context, and autonomous decision-making. How AdCP differs from OpenRTB. Understanding AdCP requires understanding what it replaces - and what it does not. OpenRTB operates on a request-response model: an SSP sends a bid request describing available inventory, DSPs respond with bids. The entire exchange is structured, deterministic, and happens in under 100 milliseconds. There is no negotiation - only a single take-it-or-leave-it auction. AdCP operates on an intent-matching model. A buyer agent expresses campaign intent (target audience, objectives, budget constraints, brand safety requirements) as structured context. A seller agent interprets that intent against available inventory, audiences, and contextual signals. The agents negotiate - they can counter-propose, ask clarifying questions, and iterate toward agreement. This is not replacing real-time bidding for standard display impressions. AdCP targets the campaign setup, deal negotiation, and strategic buying layer - the work that currently requires days of emails, IOs, and manual deal configuration. The technical architecture. AdCP is built on three layers: MCP Foundation: Anthropic's Model Context Protocol provides the agent-to-tool communication layer. MCP defines how AI agents call structured tools, receive responses, and maintain context across multi-turn interactions. AdCP Schema Layer: AdCP adds advertising-specific schemas on top of MCP - structured representations of campaign briefs, inventory descriptions, audience segments, pricing models, and deal terms. Agent Runtime: The actual buyer and seller agents that use LLMs to interpret campaign goals, match them against inventory, and negotiate terms. These agents run on infrastructure provided by the buyer (DSP) and seller (SSP/publisher) respectively. Magnite's seller agent: the first SSP implementation. Magnite was the first SSP to build a seller agent for AdCP, embedding it directly into SpringServe (their CTV ad server). The seller agent was tested in December 2025 with Scope3's buyer agent, and additional pilots followed in early 2026 with LG Ad Solutions, MiQ, and Warner Bros. Discovery. In the Magnite implementation, the seller agent uses an LLM to interpret the buyer agent's campaign intent and match it against available CTV inventory in SpringServe. When both agents agree on terms, a campaign is placed - without a human manually configuring line items, setting up deals in a UI, or exchanging insertion orders via email. The Scope3 Agentic Media Platform. Scope3's March 2026 launch formalized AdCP into a production platform with two key products: Agentic Media Platform: The core product enabling buyer agents to discover and transact with seller agents across the AdCP ecosystem. Amazon DSP is the first demand-side integration, meaning Amazon's buying infrastructure can route campaigns through AdCP-enabled seller agents. Brand Standards: A companion product that encodes brand safety and suitability requirements as structured agent context - ensuring that agentic buying decisions respect advertiser brand guidelines without requiring manual inclusion/exclusion list management. AdCP vs. AAMP: complementary, not competing. A common question: does AdCP compete with IAB Tech Lab's AAMP (Agentic Ad Management Protocols)? The short answer is no - they address different layers. AAMP provides the trust and governance framework (Agent Registry, identity verification, capability declarations) and the execution control plane (ARTF for real-time agentic decisioning). AdCP provides the agent-to-agent transaction protocol - how buyer and seller agents actually communicate campaign intent and negotiate deals. In practice, an AdCP buyer agent would be registered in AAMP's Agent Registry, use ARTF for real-time execution, and use AdCP for deal negotiation. The two frameworks are complementary. What this means practically. For DSPs: AdCP integration means your platform can express campaign requirements as structured agent intent and participate in automated deal negotiation. This is most immediately valuable for CTV and premium programmatic guaranteed deals - the high-touch, high-margin transactions that currently require manual setup. For SSPs and publishers: Building a seller agent means your inventory can be discovered and transacted by AI buyer agents without manual deal configuration. Early movers (Magnite, Index Exchange, Equativ) gain a distribution advantage as more demand flows through agentic channels. For agencies: Agentic media buying does not eliminate the agency - it eliminates the manual operational work of deal setup, IO processing, and campaign trafficking. The strategic decisions (which publishers to work with, what audiences to target, what creative strategy to deploy) remain human decisions, expressed as intent that agents execute. For advertisers: Faster campaign activation, more inventory discovery (agents can evaluate thousands of seller agents simultaneously), and potentially lower operational costs as manual campaign setup is automated. The open questions. AdCP is production-ready for CTV and premium direct deals, but several open questions remain: Liability: When an AI agent makes a buying decision that violates brand safety guidelines, who is liable - the agent operator, the LLM provider, or the platform? Audit trails: How do you audit an AI agent's negotiation reasoning? AdCP transactions need the same transparency that SupplyChain objects brought to programmatic - but for AI decision-making. Interoperability: Can a buyer agent built on GPT-4 negotiate effectively with a seller agent built on Claude? Protocol-level interoperability is defined, but LLM-level reasoning compatibility is not guaranteed. Scale: AdCP currently targets high-value, low-frequency transactions (PMP deals, PG campaigns). Whether it can scale to the millions-of-decisions-per-second pace of open-exchange RTB remains to be proven. The advertising industry has spent two decades building programmatic infrastructure for software-to-software transactions. AdCP represents the beginning of the next era: agent-to-agent transactions where AI systems negotiate, buy, and optimise media on behalf of humans. The protocol is live, the partners are real, and the production deployments are running. AdCP Scope3 Agentic AI MCP Amazon DSP Magnite Media Buying Founder, Advertising Protocols Samraj Matharu is the founder of Advertising Protocols - the open reference for ad tech standards, protocols, and industry knowledge. Explore all protocols. Deep-dive into the technical specs behind the ads you see every day.