ELECTE's Podcast: AI Frontiers
Frontier AI has outgrown the lab. The decisive questions now are about power — who builds the models, who controls them, and who gets to build on top of them. AI Frontiers is for the people doing the building: founders and operators creating products, companies, and strategy at the edge of what AI can do — on infrastructure owned by a handful of labs and governed from a handful of capitals. Each season charts where that frontier has moved, from the labs shipping the models to the capitals writing the rules, and what it means for anyone building something that lasts on ground that keeps shifting. Hosted by Fabio Lauria, founder of ELECTE. No hype, no jargon — strategy, stakes, and a builder's-eye view of the most consequential infrastructure of the century.
ELECTE's Podcast: AI Frontiers
"Tuesday at 10 AM" it's not a strategy
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"Tuesday at 10 AM" is an average, not a strategy. Fabio Lauria argues send time is a matching problem: aligning reader attention with what your email asks them to do. Benchmarks from Mailchimp and MailerLite are starting points only. Google's 2024 bulk-sender rules and Apple's Mail Privacy Protection mean deliverability and measurement must come before timing tests.
AI Frontiers is produced by ELECTE, the AI-powered analytics platform for European SMEs.
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Written and hosted by Fabio Lauria.
This is AI Frontiers. Today, why email send time is a distribution problem, not a scheduling trick. The core argument is direct. Tuesday at 10 a.m. is not a strategy. It is an average, and averages are not operating models. The article by Fabio Lauria, CEO and founder of ELECTE, makes a structural point that most send time advice ignores. Send time is a matching problem. You are matching the kind of attention a message needs with the conditions in which that attention is available. Reading, thinking, replying, registering, buying, these are different behaviors. They do not happen in the same window. That distinction collapses when you publish across borders, languages, and work cultures. A bilingual publication sent to senior readers in Milan, London, and New York does not face one audience moment. It faces several. Treating them as one mass is not optimizing, it is averaging. The benchmark survives because it is easy to package. Dashboards need defaults. Vendors benefit from rules that sound universal because universality is easy to productize. MailChimp's send time optimization guidance and mailer lights, 2025 benchmark material both still point to local time scheduling and midweek morning windows as reasonable defaults, not universal winners. That is as far as benchmark data should take you. There is also an upstream problem most teams skip. Inbox placement comes before timing. Since February 2024, Google has required bulk senders to meet authentication and hygiene standards, SPF or DKIM, DMARC for bulk traffic, one-click unsubscribe, and a spam complaint rate kept below 0.3%. Yahoo published parallel requirements on the same timetable. If those conditions are weak, send time tests produce noise. Open rates have the same problem. Since Apple introduced mail privacy protection in 2021, open data has become materially less reliable because Apple mail can preload tracking pixels regardless of user intent. Opens are weak evidence. The stronger question is, what behavior are you trying to produce? And does send time increase that behavior? The practical order is clear. Geography first, use recipient local time, not headquarters time. Language second, bilingual additions often produce different reading patterns. Rule third, executives, analysts, and policy readers do not use inboxes the same way. Engagement history. Fourth, consistent readers are a cleaner audience for timing tests than dormant names. Benchmarks are borrowed intelligence. Testing is proprietary intelligence. Build a send schedule that fits your publication. Do not borrow one from a vendor benchmark or an industry myth. That's AI Frontiers.
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