AI Prepared Daily — March 18, 2026

▸ AI/LLM · Technology.org

Mystery 1T-Parameter 'Hunter Alpha' Model Fuels DeepSeek V4 Speculation

A powerful anonymous AI model called Hunter Alpha appeared on OpenRouter on March 11 with 1 trillion parameters and a 1-million-token context window. During testing, the chatbot identified itself as 'a Chinese AI model primarily trained in Chinese' with a May 2025 knowledge cutoff matching DeepSeek's specs. The model has already processed over 160 billion tokens, sparking intense speculation it's DeepSeek V4 in stealth testing ahead of a rumored April launch.

The bottom line: If confirmed as DeepSeek V4, this signals Chinese AI labs can match or exceed Western frontier models while potentially running on non-Nvidia hardware — disrupting both the competitive and supply chain landscape.

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▸ Data Engineering · Snowflake Documentation

Snowflake Launches Openflow Connector for BigQuery in Preview

Snowflake released a new Openflow Connector for Google BigQuery in preview on March 17, enabling direct replication of datasets, tables, and views from BigQuery into Snowflake. The connector uses incremental change capture via BigQuery's native CHANGES function for efficient synchronization. This expands Snowflake's Openflow platform which already supports Oracle, MySQL, and other sources.

The bottom line: Marketing data teams running multi-cloud analytics can now consolidate BigQuery datasets into Snowflake without third-party ETL tools — simplifying cross-platform customer data integration.

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▸ Emerging Tools · TechCrunch

Eridu Raises $200M to Solve AI Data Center Network Bottleneck

AI networking startup Eridu emerged from stealth with a $200M Series A led by Socratic Partners and John Doerr. Founded by networking veteran Drew Perkins (creator of Point-to-Point Protocol, founder of Infinera), the company is building custom network switches to address the fundamental mismatch between GPU compute improvements and stagnant networking speeds. The company claims its architecture can reduce capital costs by 40% and networking power consumption by 70%.

The bottom line: As marketing AI workloads scale to millions of model calls, network infrastructure becomes a hidden cost driver — Eridu's approach could dramatically reduce inference costs for high-volume applications.

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▸ MarTech/AdTech · AdExchanger

Meta Introduces 'Engage-Through Attribution' for Social Measurement

Meta announced it will segment social engagement (shares, saves, comments, likes) into a new 'engage-through attribution' category separate from traditional click-through attribution. The click-through definition now aligns with Google Analytics by only counting final link clicks. This addresses a longstanding friction where Meta's attribution often looked stronger in-platform than in third-party reporting tools.

The bottom line: Marketing teams can finally reconcile Meta campaign performance with Google Analytics without constant 'apples-to-oranges' debates — cleaner reporting should reduce attribution friction with finance.

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In Other News

Wonderful Raises $150M at $2B Valuation for Non-English AI Agents — For global marketing teams, this signals enterprise AI agents are mature enough for complex multilingual customer interactions — expect accelerated automation of contact centers in non-English markets.TechCrunch →
Albertsons Launches In-Store Incrementality Measurement Framework — For CPG marketers, this is the proof-of-incrementality standard retail media has lacked — expect other RMNs to follow with similar frameworks, making in-store media budgets easier to justify.Albertsons Companies →