Final week, we had our first Infrastructure & Ops superstream of 2026, Platform Engineering within the Age of AI. Our audio system explored a variety of matters 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 means of what a great platform for AI seems like, Cockroach Labs’ Jordan Lewis shared classes discovered rolling out a company AI platform, Syntasso’s Daniel Bryant outlined a three-layer mannequin for constructing a great platform, expertise chief Sarah Wells mentioned the significance of governance and the way to make it extra manageable, and Thoughtworks’ Ben O’Mahony defined why evals needs to be a part of your observability story. You’ll be able to watch the highlights right here.
The occasion concluded with a fireplace chat between Sam and Nathen Harvey, who leads the DORA workforce at Google Cloud. DORA has been monitoring software program supply efficiency for over a decade, which suggests they’ve watched a whole lot of expertise traits come by means of. Their middle of gravity has at all times been the identical query: How shortly and safely can a workforce transfer change right into a operating manufacturing utility?
AI hasn’t modified that query, though it has made answering it a bit more durable. DORA lately launched its ROI of AI-Assisted Software program Growth report to indicate how AI is working for groups proper now, and the way that will or is probably not contributing to organizations’ backside strains. 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 largest headline findings from DORA’S 2025 knowledge: Organizations noticed about 10% enchancment when it comes to precise code shipped to manufacturing methods. Though builders seemingly felt that they had been extra productive, that doesn’t robotically carry by means of to manufacturing. DORA’s knowledge exhibits greater throughput alongside greater instability. In different phrases, groups are delivery extra however they’re additionally extra often 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 dangerous ones)
Nathen defined that AI is an amplifier and mirror that equally displays the nice and dangerous. On groups the place delivery change is already simple, AI tends to maintain issues operating nicely. On groups the place getting become manufacturing is painful, AI generates extra change and makes the prevailing friction extra acute. That stated, his learn on this final result 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 relatively than an invite to repair the method: Right here’s your new instrument. Now we anticipate extra from you. Addressing this drawback means reframing the query “Does AI make folks extra productive?” What we actually needs to be asking is “Beneath what situations will AI enhance productiveness, and who’s liable for creating them?” And that falls on the group, not the expertise.
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 considered one of them). Regardless of all of the advances in generative AI, these instruments nonetheless make errors, and in the event you’ve multiplied your means to generate code with out doing something to scale your means to confirm it, you’ve made your state of affairs worse, not higher.
Nathen known as this the verification tax, and it belongs in any trustworthy accounting of AI’s productiveness affect. Pipeline adaptation belongs there too: Is your supply pipeline match for objective given the amount of change you’re now attempting to push by means 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 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 faux in any other case. What it does is give groups a option to mannequin the actual prices, together with the training 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 turn out to be 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 stream. Agentic workflows are actually 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 specifically, 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 program Well being within the Age of AI.”) Right here’s how Storey explains the issue in her weblog submit:
Debt compounded from going quick lives within the brains of the builders and impacts their lived experiences and talents to “go quick” or to make modifications. Even when AI brokers produce code that might be simple to know, the people concerned could have merely misplaced the plot and should not perceive what this system is meant to do, how their intentions had been carried out, or the way to presumably change it.
And as Sam famous, this compounds throughout groups and organizations. As builders more and more work in parallel with AI relatively than with one another, they lose the shared understanding that comes from folks constructing software program collectively. Kent Beck as soon as stated that “software program design is an train in human relationships.” Agentic workflows are placing stress on that in methods we’re solely starting to see.
Nathen agreed cognitive debt is the place he’s most involved, and each your staff 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.
Spend money on 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 means of the record, ending on high quality inside 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 concentrate on the platform.
His argument is that platforms matter extra, not much less, in an surroundings the place AI makes it potential for nearly anybody in a company to construct one thing. The folks closest to clients and enterprise issues can now generate working software program. What they will’t do is make sure that software program is sturdy, safe, and production-ready.
Nathen advised that the most effective leverage for software program engineering funding as we speak is likely to be constructing platforms that present these guardrails, that shift the complexity of production-readiness down into the infrastructure in order that anybody constructing on high of it will get the protection web without cost. He acknowledged that shifting each product engineer to platform work is likely to be overkill. However the path of journey is actual. The platform can also be, as Newman identified, the place you carry determinism again right into a course of that AI has made extra nondeterministic.
That’s one thing we’ve been listening to loads right here at O’Reilly. The enlargement of who can construct doesn’t cut back the necessity for deep engineering experience. It modifications the place that experience is most beneficial, and platforms are a great reply to the place.
What DORA’s analysis tells us
The groups which might be doing nicely are operating experiments, studying from them, and spreading these classes. The measure Nathen advised isn’t what number of tokens you’ve consumed however what number of experiments you’ve run and the way nicely you’re distributing what you’ve discovered.
The instruments are shifting quick sufficient that any group locking in a hard and fast 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 out there at dora.dev, together with the 2025 annual report and the ROI framework. The DORA Group gives an area for practitioners to work by means of these questions collectively. If you happen to’re attempting to navigate any of this along with your workforce, you could wish to spend a while there.
And if you wish to dive deeper into Nathen and Sam’s chat or discover the opposite periods, you possibly can watch your entire Infrastructure & Ops Superstream on the O’Reilly studying platform. Our subsequent occasion, on September 9, will cowl agentic observability. Register without cost right here, and take a look at all the opposite free dwell occasions on O’Reilly.
