There’s a mental shift that happens when you go cloud-first. At some point, you stop asking “should we use the cloud for this?” and start asking “why wouldn’t we?” The default flips. Infrastructure decisions that used to start with rack-and-stack now start with a region picker.
I’ve watched this happen across dozens of customers. The transition isn’t instant. It follows a pattern: skepticism, experimentation, selective adoption, then a moment where the old way just feels wrong. Once a team has experienced spinning up environments in minutes instead of weeks, they can’t go back. The friction of the old model becomes intolerable.
The same shift is happening with AI-augmented workflows. And it’s happening faster than cloud did.
The Emerging Default
Right now, most knowledge workers treat AI as a tool they reach for occasionally. Summarize this doc. Draft this email. Help me debug this code. It’s useful, but it’s supplemental. The mental model is still: I do the work, AI helps sometimes.
But there’s a growing segment of people, and I’m one of them, where the default has already flipped. The question isn’t “should I use AI for this?” It’s “why would I do this manually?” Research, first drafts, data analysis, scheduling, code scaffolding, even thinking through problems. The starting point is agentic. The manual path is the exception that needs justification.
This is exactly how cloud-first thinking emerged. Not because cloud was perfect for every workload, but because the productivity delta made the old default feel irrational.
Not Everything Has Moved (Yet)
Here’s where the analogy holds in a way people don’t talk about enough: cloud-first didn’t mean everything moved to cloud. Plenty of workloads stayed on-prem for good reasons. Latency requirements, regulatory constraints, cost profiles that didn’t favor cloud economics, or simply maturity gaps in managed services.
The same is true for AI-first workflows today. Not every task has a reliable agentic solution. High-stakes decisions still need human judgment. Creative work still needs human taste. Anything requiring deep institutional trust or nuanced relationship management isn’t getting delegated to an agent anytime soon.
But that doesn’t matter for the broader point. Cloud-first won not because it was universally superior, but because it became the rational default for a large enough set of use cases that the mindset shifted. The remaining on-prem workloads became the exception, not the rule.
AI-first is reaching that same threshold.
The Trust Curve
Cloud adoption followed a trust curve that took roughly a decade to mature:
- Skepticism. “It’s not secure. It’s not reliable. We can’t put real workloads there.”
- Experimentation. “Let’s try dev/test. Maybe some static websites.”
- Selective adoption. “OK, new projects start in cloud. We’ll migrate the rest case-by-case.”
- Default mindset. “Everything goes to cloud unless there’s a specific reason not to.”
AI workflows are somewhere between stage 2 and 3 for most organizations. The early adopters, both individuals and teams, are already at stage 4. They’ve crossed the threshold where the productivity difference is so significant that reverting feels like going back to provisioning physical servers.
The Irreversibility Problem
This is the part that matters most. Once you’ve experienced the delta, you can’t unsee it.
A developer who’s been pair-programming with AI for six months doesn’t go back to writing everything from scratch. A researcher who uses AI to synthesize papers and surface patterns doesn’t go back to reading everything linearly. A leader who uses AI to prep for meetings, draft communications, and track priorities doesn’t go back to doing it all manually.
The same way a team that’s deployed with CI/CD pipelines to cloud infrastructure doesn’t go back to manually configuring servers.
The gap between “AI-augmented” and “manual” will only widen. And the people who’ve already made the shift will have a compounding advantage over those who haven’t, just like cloud-native companies had over those still running on-prem by 2018.
What This Means
If you’re in technology leadership, the implication is straightforward: AI-first thinking is becoming a default, not an experiment. The question isn’t whether your team will adopt it, but whether they’ll adopt it early enough to benefit from the compounding, or late enough that they’re playing catch-up.
The workloads that stay “on-prem,” the tasks that remain fully manual, will shrink over time, just as on-prem footprints have. Not to zero. But enough that the default has already changed for the people paying attention.
The emerging default is here. The only question is whether you’ve noticed.