Antonio Mallia: AI Agents Need a Different Kind of Search | Ep 1305
Descripción del Episodio
In this episode of The Digital Executive Podcast, host Brian Thomas sits down with Antonio Mallia, founder and CEO of Seltz.ai, to explore one of the most overlooked infrastructure questions in the AI era: what happens to web search when the user is no longer a human? Antonio opens with a career that runs from the University of Pisa's information retrieval tradition through a PhD at NYU to research roles at Amazon, Pinecone, and Bloomberg — and traces the moment the idea for Seltz crystallized while building Alexa's question answering engine, when it became clear that the next consumer of search results was not a person but a machine. He then explains why Seltz took the harder path of building its own full retrieval stack from scratch rather than wrapping Google or Bing like most AI search products — and why that distinction matters deeply. Traditional search engines were optimized for human patience: ranked lists, short snippets, spell correction, ambiguous queries refined over multiple attempts. LLMs need something fundamentally different — full paragraphs without truncation, long and specific queries that nail the topic on the first attempt, and above all, freshness, because the one thing a model cannot learn in training is what happened after its cutoff date. Seltz also focuses its index on the content LLMs struggle most with — tables, financial records, SEC filings, and structured data that traditional crawlers skip and models cannot reliably memorize. Antonio closes with a prediction that reframes the entire stakes of what he is building: human search queries have plateaued, but machine queries are growing exponentially, and as people spawn multiple specialized agents to handle their knowledge work, the query load on web infrastructure will reach a scale never seen before. The companies that build for that future — with efficiency and freshness at their core — will define the next twenty years of the web.
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