2025: The Year AI Became the New Cloud - How Experimental Tech Becomes Mainstream

In the ever-shifting landscape of technology, certain patterns emerge with remarkable consistency. Technologies that begin as experimental curiosities often evolve into the very foundation upon which mainstream engineering is built. In 2010, that transition was clearly visible with cloud computing. Today, in 2025, we're witnessing a similar transformation with artificial intelligence.

The author, a veteran of the tech industry who witnessed the cloud's rise firsthand at Linden Lab, recalls a time when Amazon's EC2 and S3 were just emerging technologies. "We were running Second Life out of three datacenters, where we racked and stacked all the servers ourselves," they write. "In desperation, we spun up an experimental project to try using S3 as our asset store. Maybe we could make this Amazon's problem and buy ourselves some time?"

This experimental approach quickly became standard practice. "Other 'experimental' projects sprouted up like weeds: rebuilding server images in the cloud, running tests, storing backups, load testing, dev workstations. Everybody had shit they wanted to do that exceeded our supply of datacenter resources."

By 2010, the center of gravity had shifted. "Instead of 'mainstream engineering' (datacenters) and 'experimental' (cloud), there was 'mainstream engineering' (cloud) and 'legacy, shut it all down' (datacenters)."

This historical context becomes particularly relevant when examining the current state of AI. Just eight months ago, the author delivered a closing keynote at SRECon titled "AIOps: Prove It! An Open Letter to Vendors Selling AI for SREs." The message was directed not at vendors, but at fellow SREs: "We're saying to a room of professional technological pessimists that AI needs them to engage. That their realism and attention to risk is more important than ever."

At that time, AI still felt separable from mainstream technology—a satellite orbiting the core rather than part of it itself. "People would gripe about conferences stacking the lineup with AI sessions, and AI getting shoehorned into every keynote," the author notes.

Yet, in remarkably short order, that perception has changed. "This year was for AI what 2010 was for the cloud: the year when AI stopped being satellite, experimental tech and started being the mainstream, foundational technology. At least in the world of developer tools."

This transition doesn't occur without skepticism. The industry has seen its share of hype trains: dotcom, cloud, crypto, blockchain, IoT, web3, metaverse. "I understand why people are cynical—why some are even actively looking for reasons to believe it's a mirage," the author acknowledges.

However, the existence of hype doesn't invalidate underlying value. "It doesn't mean there isn't a bubble. Of COURSE there's a fucking bubble. Cloud was a bubble. The internet was a bubble. Every massive new driver of innovation has come with its own frothy hype wave. But the existence of froth doesn't disprove the existence of value."

The parallel between cloud computing and AI extends beyond their transition from experimental to mainstream. Both technologies fundamentally changed how developers approach problem-solving, resource allocation, and system architecture. Cloud computing abstracted away infrastructure management; AI is abstracting away knowledge work and pattern recognition.

For developers and engineers, this shift demands adaptation. Those who once dismissed cloud technologies as experimental now build their careers on cloud-native architectures. Similarly, the current wave of AI integration, despite its imperfections, represents a fundamental shift in how software will be built and maintained.

The author's personal journey from cloud skeptic to AI advocate mirrors this broader industry transition. "That was just eight months ago, and AI still felt like it was somehow separable... But for me, this year was for AI what 2010 was for the cloud... I'm here now, and I'm excited. It's an exciting time to be a builder."

As we move forward, the challenge lies not in resisting technological change but in engaging with it critically. The skepticism of experienced engineers is valuable precisely because it prevents the unquestioning adoption of flawed solutions. However, this skepticism must be informed by deep understanding rather than reflexive opposition.

The story of cloud computing's evolution offers a roadmap for how AI might mature—from experimental curiosity to essential infrastructure. Along the way, there will be bubbles and busts, but the underlying transformation will continue to reshape our digital world.

For today's developers, the lesson is clear: AI is no longer a peripheral technology but a fundamental tool in the engineering toolkit. The question is no longer whether to adopt it, but how to do so wisely and effectively.