Matei Zaharia, the co-founder and Chief Technology Officer of data and AI giant Databricks, has been named the 2026 recipient of the prestigious ACM Prize in Computing. The award, announced by the Association for Computing Machinery, recognises his collective contributions to the field, most notably the creation of the open-source big data processing engine Apache Spark.
The 39-year-old executive and associate professor at UC Berkeley told TechCrunch the notification email was a surprise. The prize includes a $250,000 cash award, which Zaharia plans to donate to a charity yet to be determined.
From PhD Project to $134 Billion Company
Zaharia developed the core technology for Spark during his PhD studies at UC Berkeley under Professor Ion Stoica, launching it as an open-source project in 2009. At a time when "big data" was the dominant tech paradigm, Spark revolutionised data processing speeds and became a foundational technology for the industry.
This work formed the technical bedrock for Databricks, the company he co-founded. Under his engineering leadership, Databricks has grown into a cloud storage and AI data platform behemoth, raising over $20 billion and achieving a valuation of $134 billion with annual revenue of $5.4 billion.
'AGI is Here Already'
Looking forward, Zaharia shared a provocative perspective on artificial general intelligence (AGI). "AGI is here already. It’s just not in a form that we appreciate," he stated. He argued against applying human standards to AI models, noting that an AI's ability to pass knowledge-based exams like the bar does not equate to human-like general intelligence.
He highlighted the risks of this anthropomorphism, using the popular AI agent OpenClaw as an example. While praising its capabilities, he called it "a security nightmare" because its design to mimic a trusted human assistant leads to risks like unauthorised financial transactions or password exposure.
The Future is AI for Research
Zaharia's primary excitement lies in AI's potential to automate and enhance research across fields from biology to data science. He envisions a future of "accurate, no-hallucinations AI-powered research" becoming universal.
"The thing that I’m most excited about is what I’d call AI for search, but specifically for research or engineering," he said. He foresees AI leveraging its strengths in areas like diagnosing mechanical problems, analysing non-text data like radio waves, and simulating molecular-level changes to predict outcomes—a technique he notes his students are already exploring.