Amazon.com Inc.

03/11/2026 | News release | Distributed by Public on 03/11/2026 09:05

How three startups helped Amazon invent cloud computing and paved the way for AI

Key takeaways

  • AWSlaunched in 2006 with S3 storage, solving startups' biggest problem: where to store data.
  • Early AWS customers transformed how tech companies operate without massive upfront costs.
  • The same cloud infrastructure from 2006 now powers today's AIrevolution and innovations.
In early 2006, Don Alvarez walked into a nondescript Amazon building somewhere in downtown Seattle. He walked out with his head spinning.
"I couldn't believe the incredible power Amazon had just put at my fingertips," Alvarez recalls.
In that moment, Alvarez-an engineering expert with a Ph.D. in physics who has since built the technology behind several cloud-powered start-ups-had just become one of the first people in the world to test Amazon Simple Storage Service (S3), a then-confidential beta that would enable anyone to store and retrieve unlimited data from anywhere on the web.
Today, it's hard to appreciate how disruptive that moment was. "The ability to have an infinite amount of low-cost storage in the cloud was as revolutionary to the economics of startup businesses as generative AI is today," says Alvarez, who is now developing an AI startup in the medical space. "Both of these completely changed the cost structure for building and operating tech companies."
The transformation S3 drove was so profound that we barely remember the world before it. Prior to 2006, if you wanted to launch a tech startup, you needed to buy physical machines to hold your data. You had to forecast capacity, invest tens of thousands of dollars in hardware, and hope you got it right. Plan too little, and you'd miss your growth moment. Plan too much, and you might not survive long enough to reach it.

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"To be able to step into a data trial that early and build a product on top of it was revolutionary," says Alvarez, whose startup at the time, FilmmakerLive, was building tools to help moviemakers storyboard projects remotely. "AWS had a caliber of excellence from the beginning."
Neither Alvarez nor anyone at Amazon could have predicted that the storage service he was testing would become the foundation for something even more transformative: the artificial intelligence systems reshaping every industry today. The S3 service that solved his immediate problem in 2006 now stores the massive datasets-often measured in petabytes-that train generative AI models capable of writing code, diagnosing diseases, and holding human-like conversations.
"The scale of the impact of S3 was absolutely comparable to the revolution we're seeing with AI today," Alvarez reflects.
The evolution mirrors broader shifts in computing itself. When AWS launched publicly on March 14, 2006, it offered just one service: S3 for storage. Elastic Compute Cloud (EC2) for servers followed a few months later. Today, AWS provides more than 240 servicesspanning everything from databases to quantum computing to machine learning. What started as "rent a server, store a file" has become "deploy an AI agent that can reason, plan, and execute complex tasks autonomously."
Alvarez was one of three customers featured in the original AWS launch announcement. The other two-Nathan McFarland of CastingWords and Andrew Westphal of UC Berkeley's Stardust@home project-faced similarly daunting data challenges.

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McFarland's transcription service was trying to disrupt an industry that required customers to mail in CDs. When the company's main server-"about six spinning discs in a room somewhere in San Francisco"-crashed in 2006, he uploaded everything to S3. "The costs of storage and bandwidth just vanished overnight," he said at the time. His business model presaged today's AI-powered speech recognition.
Westphal's challenge was finding interstellar dust particles roughly one micrometer (a millionth of a meter) in size by crowdsourcing volunteers to examine millions of microscope images. S3 made those images accessible to more than 34,000 citizen scientists who've contributed 130 million searches. This kind of distributed human intelligence solving complex visual problems now has a powerful AI complement in computer vision models that can analyze similar datasets in seconds.
"What wasn't obvious to me then was just how many different types of organizations, everywhere in the world, would have a use for it," McFarland reflected in 2021. "You no longer needed tens of thousands of dollars to invest in hardware when you weren't even sure if something was going to work."
For Alvarez, watching that principle extend to AI has been remarkable. "The same de-risking that AWS brought to infrastructure, it's now bringing to intelligence itself," he says. "In 2006, I didn't want to think about managing servers. Today's developers don't want to think about provisioning GPUs or optimizing inference pipelines. They just want their AI applications to work."

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That's the pattern those earliest AWS customers established: infrastructure should be invisible, reliable, and accessible to anyone with an idea. The barrier shouldn't be capital or technical expertise-it should simply be imagination.
"When I walked out of that building in Seattle in 2006, I remember thinking about what I could build if storage was essentially infinite and instantly accessible," Alvarez says. "Today, people are asking the same question about intelligence. What could I build if intelligence was infinite and instantly accessible? And thanks to what we helped start 20 years ago, they're finding out."
Two decades on, these three disparate customers prove that the hardest problems often share common solutions. And sometimes, the infrastructure you build to solve the challenges of today becomes the launchpad for innovation you never dreamed possible.
Learn more about Amazon Simple Storage Service (S3)and catch up on AWS News.

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