Steve Yen, Co-founder, Couchbase – 2026 Predictions

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Recent developments in AI have opened up wide possibilities and opportunities, helping developers optimize code for cost-effective and scalable workloads. Data generation has also introduced significant challenges, creating major risks for organizations that lack modern, JSON-first data platforms. Steve Yen, co-founder of Couchbase, shares his insights on what to expect in 2026.

Prediction 1:

Before the AI bubble pops, you can be ready ahead of the game.

Whether the AI bubble bursts in 2026 or 2027, forward-looking teams can prepare now for the opportunities that follow. As technologists, we’ve seen this pattern before: once the hype cools, infrastructure becomes dramatically more accessible. That means more GPUs, more distributed data centers, more storage, more electricity, more edge processing and more capacity overall, all at price points that open new doors for innovation. Smart organizations will treat this as a moment to plan, so they’re ready to capitalize on the windfall on the other side of the bubble.

At Couchbase, we expect this GPU surplus to accelerate what our database can deliver. We see a future of incredibly fast, massively parallel processing for operational workloads, differentiated data analysis capabilities, highly scalable vector indexing and search, and new ways to serve AI-powered applications at global scale. With GPUs, memory, storage and networking becoming cheaper and more widespread, the price-performance curve shifts in favor of builders. And we couldn’t be more excited about what that unlocks.

Prediction 2:

The next-gen of developers will act more like conductors guiding fast-moving teams.

The day-to-day work of a developer is shifting. Developers who want to stay ahead will use AI the way a head chef runs a busy kitchen, directing parallel tasks, comparing multiple options, deciding what’s worth keeping and pushing work forward quickly. The real skill is orchestration, not trying to personally hand-craft every line of code. That shift will help teams ship faster and stay relevant.

The biggest advantage will come from understanding the higher levels of the system: how data flows, how subsystems behave under load and how to keep the bigger picture in focus across an increasingly distributed world that spans edge and cloud. Developer data platforms that support quick iteration, flexible data models and reliable edge-to-cloud performance will give teams what they need to supervise and collaborate with AI so they can move faster than the competition.

Prediction 3:

A new kind of AI slop will spike as companies generate data faster than they can manage

The messy part of AI for the enterprise won’t be the goofy content everyone jokes about. The real issue is the surge of semi-structured and regenerated data that AI produces, and the companies that shore up their data foundations now will be in the best position to take advantage of it. As business teams start building features, rewriting content and generating new forms of data on their own, AI will create new tables, new fields and new analytical artifacts at a pace older systems were never designed to handle. Without the right data infrastructure, the result is confusion, duplicated information and a steady drop in confidence in what the data is telling you.

To keep this from turning into chaos, enterprises need systems that can absorb constant structural changes, handle heavy ingest and support fast iteration. They also need ways to keep AI grounded in reliable operational data so outputs don’t drift or degrade. Pairing flexible JSON models with capabilities like vector search and production-grade scalability gives teams a practical way to move faster than the competition.

Prediction 4:

The gap between GPU-rich and GPU-poor companies will define the next phase of AI

The gap between the GPU haves and have-nots is going to get more obvious. The GPU-rich players are the hyperscalers and model labs that can afford data centers full of H100s and Blackwells. Everyone else is left figuring out what they can run without that level of compute. If you don’t have the GPUs, there are entire areas of AI you simply can’t touch right now.

But this divide won’t last. If the AI bubble cools, all that GPU capacity and all those new data centers don’t vanish. They become available to the broader market at far more reasonable prices. That shift creates space for teams that aren’t training giant models but still want to build meaningful AI features on top of the expanding infrastructure stack. It also puts more attention on platforms that can store, sync and reshape the large volumes of data AI produces without consuming half the budget on compute.

Prediction 5:

AI will push development cycles from months to days, and data platforms will need to keep up

AI is going to speed up the way software ships. Business teams can already spin up prototypes or new features without waiting on developers, and that pace will only increase. Work that used to take months may compress into days or even hours when AI produces the first draft. Developers become reviewers and coordinators rather than being the sole point of execution.

That speed puts real pressure on the data layer. Schemas will change constantly. New fields and new collections will appear overnight. Applications will grow and shift in ways older systems weren’t built to absorb. Teams will need data platforms that can handle rapid iteration, fast rollback and constant updates without putting production at risk. JSON-first databases with high ingest rates, support for quick structural changes and reliable edge sync will match the pace of AI-driven development. Systems tied to rigid structures will fall behind as soon as the cycle accelerates.