Final week, we had our first Infrastructure & Ops superstream of 2026, Platform Engineering in the Age of AI. Our audio system explored a spread of subjects targeted on supporting new AI workloads, every with distinctive infrastructure wants, unpredictable prices, and novel safety issues. Google Cloud’s Abdel Sghiouar took the viewers by way of what an excellent platform for AI appears to be like like, Cockroach Labs’ Jordan Lewis shared classes realized rolling out a company AI platform, Syntasso’s Daniel Bryant outlined a three-layer mannequin for constructing an excellent platform, know-how chief Sarah Wells mentioned the significance of governance and how you can make it extra manageable, and Thoughtworks’ Ben O’Mahony defined why evals needs to be a part of your observability story. You may watch the highlights here.
The occasion concluded with a hearth chat between Sam and Nathen Harvey, who leads the DORA group at Google Cloud. DORA has been monitoring software program supply efficiency for over a decade, which suggests they’ve watched a whole lot of know-how tendencies come by way of. Their middle of gravity has at all times been the identical query: How rapidly and safely can a group transfer change right into a operating manufacturing software?
AI hasn’t modified that query, though it has made answering it a bit more durable. DORA just lately launched its ROI of AI-Assisted Software Development report to indicate how AI is working for groups proper now, and the way that will or is probably not contributing to organizations’ backside traces. Nathen used the findings as a jumping-off level to dig into how AI is altering platform engineering and software program growth as a complete.
The productiveness hole
Sam began by declaring one of many greatest headline findings from DORA’S 2025 knowledge: Organizations noticed about 10% enchancment when it comes to precise code shipped to manufacturing programs. Although builders seemingly felt that they had been extra productive, that doesn’t robotically carry by way of to manufacturing. DORA’s knowledge exhibits increased throughput alongside increased instability. In different phrases, groups are transport extra however they’re additionally extra incessantly rolling again modifications or implementing fixes. The positive aspects on the particular person degree are actual (and 10% is a reasonably good quantity), however these positive aspects aren’t “the dramatic enhancements that you just discover within the headlines.”
AI amplifies good processes (and unhealthy ones)
Nathen defined that AI is an amplifier and mirror that equally displays the nice and unhealthy. On groups the place transport change is already straightforward, AI tends to maintain issues operating effectively. On groups the place getting develop into manufacturing is painful, AI generates extra change and makes the prevailing friction extra acute. That stated, his learn on this consequence is cautiously optimistic: “If the ache is extra acute, we perhaps will spend money on addressing that ache.”
The rub is that the funding has to truly occur. Nathen famous that in lower-performing organizations, AI instruments usually arrive with a reset of expectations moderately than an invite to repair the method: Right here’s your new device. Now we count on extra from you. Addressing this downside means reframing the query “Does AI make individuals extra productive?” What we actually needs to be asking is “Underneath what circumstances will AI increase productiveness, and who’s liable for creating them?” And that falls on the group, not the know-how.
Verification isn’t a checkbox
Belief is an enormous problem with generative AI. About 30% of DORA survey respondents belief AI output little or in no way. Round 46% belief it “considerably” (and Nathen is one among them). Regardless of all of the advances in generative AI, these instruments nonetheless make errors, and should you’ve multiplied your potential to generate code with out doing something to scale your potential to confirm it, you’ve made your scenario worse, not higher.
Nathen known as this the verification tax, and it belongs in any trustworthy accounting of AI’s productiveness impression. Pipeline adaptation belongs there too: Is your supply pipeline match for function given the amount of change you’re now attempting to push by way of? These prices don’t present up within the headlines about 10x developer productiveness. They present up in your incident reviews three months later.
DORA just lately revealed an ROI framework and calculator for AI-assisted software program growth. Nathen was clear that there’s no common quantity to supply, and the calculator doesn’t fake in any other case. What it does is give groups a technique to mannequin the true prices, together with the educational funding, the verification overhead, and the pipeline modifications required.
Context switching and burnout
With productiveness on the upswing, AI-induced burnout is changing into a critical concern. (Steve Yegge calls this the “AI vampire.”) DORA’s knowledge for 2025 confirmed that AI adoption wasn’t strongly related with burnout, with the caveat that about 64% of DORA survey respondents stated they’d by no means labored in an agentic workflow. Each of these findings are prone to change considerably in 2026.
Nathen highlighted one supply of burnout he expects to escalate as brokers change into the norm: context switching. As he identified, software program builders spent years arguing for protected focus time to do the deep work that requires them to take care of circulation. Agentic workflows at the moment are incentivizing those self same builders to voluntarily run a dozen or extra brokers without delay, forcing them to context-switch a number of occasions each hour. As he joked, “There’s loads of analysis that helps the concept all of us really feel like we’re fairly good multitaskers and none of us are.” The implications are coming, and we’re doing it to ourselves.
The cognitive debt query
Sam Newman introduced up the associated notion of “cognitive debt,” and particularly, Margaret-Anne Storey’s dialogue of it. (See “How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt” and “From Technical Debt to Cognitive and Intent Debt: Rethinking Software Health in the Age of AI.”) Right here’s how Storey explains the issue in her weblog put up:
Debt compounded from going quick lives within the brains of the builders and impacts their lived experiences and skills to “go quick” or to make modifications. Even when AI brokers produce code that might be straightforward to grasp, the people concerned might have merely misplaced the plot and will not perceive what this system is meant to do, how their intentions had been applied, or how you can presumably change it.
And as Sam famous, this compounds throughout groups and organizations. As builders more and more work in parallel with AI moderately than with one another, they lose the shared understanding that comes from individuals constructing software program collectively. Kent Beck as soon as stated that “software design is an exercise in human relationships.” Agentic workflows are placing strain on that in methods we’re solely starting to see.
Nathen agreed cognitive debt is the place he’s most involved, and each your employees and your structure will undergo for it. Understanding the ramifications of an architectural determination you made eight months in the past takes years of operation to floor, and AI doesn’t assist with that in any respect.
Put money into your platform now
Contemplating what makes some AI-assisted groups excessive performers, Nathen defined, “It’s not that you’re utilizing AI however how you’re utilizing AI.” This remark led DORA to develop seven capabilities that, when mixed with AI adoption, result in higher outcomes. Nathen briefly ran by way of the checklist, ending on high quality inner platforms. And right here he made a declare about software program engineering funding that was, in his phrases, “just a little bit wild”:
Each product engineer that you’ve in your group, each engineer that’s targeted on constructing options proper now, ought to in all probability cease constructing options and deal with the platform.
His argument is that platforms matter extra, not much less, in an setting the place AI makes it attainable for nearly anybody in a corporation to construct one thing. The individuals closest to prospects and enterprise issues can now generate working software program. What they’ll’t do is be sure that software program is sturdy, safe, and production-ready.
Nathen instructed that the perfect leverage for software program engineering funding right now could be constructing platforms that present these guardrails, that shift the complexity of production-readiness down into the infrastructure in order that anybody constructing on prime of it will get the security internet at no cost. He acknowledged that transferring each product engineer to platform work could be overkill. However the path of journey is actual. The platform can be, as Newman identified, the place you deliver determinism again right into a course of that AI has made extra nondeterministic.
That’s one thing we’ve been listening to quite a bit right here at O’Reilly. The growth of who can construct doesn’t cut back the necessity for deep engineering experience. It modifications the place that experience is Most worthy, and platforms are an excellent reply to the place.
What DORA’s analysis tells us
The groups which can be doing effectively are operating experiments, studying from them, and spreading these classes. The measure Nathen instructed will not be what number of tokens you’ve consumed however what number of experiments you’ve run and the way effectively you’re distributing what you’ve realized.
The instruments are transferring quick sufficient that any group locking in a set coverage round particular instruments will discover itself caught. What you need is the capability to continue learning, which suggests constructing the tradition and the processes that make studying seen and transferable.
All of DORA’s analysis is freely obtainable at dora.dev, together with the 2025 annual report and the ROI framework. The DORA Community offers an area for practitioners to work by way of these questions collectively. In the event you’re attempting to navigate any of this along with your group, chances are you’ll need to spend a while there.
And if you wish to dive deeper into Nathen and Sam’s chat or discover the opposite periods, you may watch the entire Infrastructure & Ops Superstream on the O’Reilly studying platform. Our subsequent occasion, on September 9, will cowl agentic observability. Register at no cost right here, and take a look at all the opposite free stay occasions on O’Reilly.