For years, technology organizations have sought to answer a simple question: Are we delivering faster, more reliably?
For years, technology organizations have sought to answer a simple question: Are we delivering faster, more reliably?
This is precisely what DORA metrics were able to measure. They provided a common language to evaluate the performance of engineering teams through deployment frequency, lead time, change failure rate and recovery time.
But the massive arrival of AI in the software development cycle is profoundly changing the nature of the subject. Performance is no longer limited to pipeline speed. It now also depends on the ability of developers to stay focused, to understand what they are producing, to maintain a high level of quality and to preserve their cognitive capital.
In other words: DORA measures execution performance very well. SPACE allows us to better understand the performance of the human system.
DORA: the delivery thermometer
DORA metrics have played a major role in the professionalization of DevOps. They made it possible to move away from a subjective approach to performance and make it observable.
DORA answers essential questions:
Do we deliver often?
Do we deliver quickly?
Are our changes breaking production?
Do we quickly restore service in the event of an incident?
These indicators are valuable because they reflect an organization’s ability to transform an intention into delivered value. They measure pipeline efficiency, operational stability, and execution maturity.
In a pre-AI world, this reading was already very powerful. A team that could deploy frequently, with a low failure rate and short recovery time, was often considered successful.
But in the age of AI, this reading becomes incomplete.
For what ? Because a team can deliver faster while generating more fatigue, overload, interruptions, cognitive debt or invisible complexity.
AI can speed up code production. It can reduce the time needed to write a function, generate tests, document an API, or propose a fix. But this acceleration of flow does not automatically guarantee an improvement in the overall system.
It can even produce the opposite effect: more code to reread, more decisions to validate, more dependencies to understand, more risks to arbitrate.
This is the limit of “all-speed”.
DORA measures the technical result. But DORA does not always say whether the developer worked in good conditions, whether he maintained his flow, whether he really understood what he validated, nor whether the team is accumulating an unsustainable cognitive load.
AI: flow accelerator, but also complexity amplifier
The infographic highlights a key point: AI increases throughput, but also increases the need for stability.
In development teams, AI assistants can give the impression of an immediate win. The code arrives faster. There are many suggestions. Prototypes can be built in just a few minutes. Developers can explore more options, speed up certain repetitive tasks, and reduce time spent on mechanical activities.
But this speed creates a new responsibility: verification.
The faster AI produces, the faster humans must be able to evaluate. However, proofreading, understanding, testing, securing and maintaining code generated or assisted by AI requires significant attention. The bottleneck is moving.
Before, the main constraint was often writing the code.
Tomorrow, it will be more and more in validation, architecture, security, review and the ability to distinguish a good suggestion from a bad one.
This is where software performance changes in nature. It is no longer just a question of delivery. It becomes a question of balance between speed, confidence and cognitive sustainability.
SPACE: measuring the human, not just the pipeline
The SPACE framework completes this vision by introducing a broader reading of developer performance. SPACE is based on five dimensions: satisfaction, performance, activity, communication/collaboration and efficiency/flow.
Its strength is to point out something that is too often forgotten: a successful developer is not simply a developer who produces more lines of code or closes more tickets.
A successful developer is one who understands context, makes good decisions, collaborates effectively, stays focused on the right things, and produces lasting value.
SPACE therefore makes it possible to measure what DORA does not always see:
the quality of the developer experience;
the level of friction in tools and processes;
the quality of the collaboration;
the ability to stay in flow;
the cognitive load experienced;
team satisfaction and commitment.
In the age of AI, these dimensions become central. Because if AI tools increase production capacity, they also increase the volume of decisions to be made. The developer is not replaced by AI; he becomes more of a supervisor, an architect, a validator and an integrator of proposals.
This change requires a new way of measuring performance.
Flow vs friction: the real battleground
One of the most important messages of infographics is that of flow.
Flow represents this state in which the developer can move forward without excessive interruption, with a clear understanding of his objective, a fluid environment and consistent tools.
Friction, on the other hand, corresponds to anything that breaks this dynamic: context changes, scattered tools, unobtainable documentation, overly cumbersome processes, redundant validations, permanent notifications, poorly formulated tickets, unstable environments.
AI does not automatically remove this friction. In some cases, it can even increase it.
If each tool adds its own wizard, its own chat, its own build mode, and its own recommendations, the developer can find themselves facing a new layer of complexity. It’s no longer just about coding, but about managing an ecosystem of assistants, checking their output and deciding between several suggestions.
So the topic is not just: “Do we have AI in our SDLC?”
The real question is: “Does AI actually reduce friction or does it add new cognitive load?”
The verification tax: the hidden cost of generative AI
The infographic introduces a particularly important concept: the verification tax.
AI generates quickly. But what it generates must be reread, understood, tested, secured and maintained. This step becomes strategic.
An organization that only measures the volume of code produced risks making a big mistake. It may believe that it has gained in productivity when it has simply shifted the effort towards review, correction, validation or exploitation.
So the real question is not: how much code does AI allow us to produce?
The real question is: how much reliable, useful, maintainable and compliant code are we able to integrate without degrading the system?
This is where DORA and SPACE should be read together.
DORA will say if the delivery flow is improving.
SPACE will say if this improvement is sustainable for the teams.
Without SPACE, we risk driving at speed.
Without DORA, we risk steering by feeling.
With both, we begin to manage software performance in a more complete way.
DORA and SPACE: two complementary readings
The opposition between DORA and SPACE is actually misleading. You don’t have to choose one against the other. We must understand their complementarity.
DORA answers the question: what results are we producing?
SPACE answers the question: under what conditions do we produce them?
DORA looks at speed and stability.
SPACE looks at well-being, flow and real efficiency.
DORA sees AI as a potential throughput amplifier.
SPACE invites us to also look at it as a potential risk of cognitive load.
This double reading becomes essential for technological departments. Because the challenge is no longer just to accelerate delivery. The challenge is to create an engineering system capable of remaining efficient over time.
An organization that accelerates without protecting its developers prepares an invisible debt.
An organization that protects flow without measuring results risks lacking impact.
A mature organization must do both.
What tech leaders need to change
For CTOs, CIOs, engineering managers, platform teams and DevX leaders, the message is clear: the AI era requires a review of performance dashboards.
It is no longer enough to follow traditional delivery indicators. To this must be added indicators of developer experience, friction, quality of collaboration and cognitive load.
Concretely, this means measuring:
the time actually spent in deep development;
the number of interruptions and context switches;
the perceived quality of AI tools;
the time to review the generated code;
the rate of rejection or correction of AI suggestions;
developer satisfaction;
confidence in the pipeline;
the quality of interactions between development, security and operations.
These indicators do not replace DORA metrics. They complete them. They allow us to understand whether the acceleration produced by AI is a real systemic improvement or simply a temporary increase in throughput at the cost of increased fatigue.
Towards a new definition of developer performance
In the age of AI, developer performance can no longer be reduced to production logic. It becomes a collective capacity to produce quickly, well, sustainably and intelligently.
The AI-augmented developer is not only faster. It must be better equipped, better protected, better integrated into a clearer decision-making system.
Modern performance is based on three balances:
Speed and stability: deliver quickly without degrading production.
Automation and human judgment: using AI without giving up technical responsibility.
Productivity and cognitive health: increase throughput without exhausting teams.
This is precisely where DORA and SPACE become complementary. DORA gives the delivery thermometer. SPACE acts as a shield for cognitive capital.
Conclusion: measure less to control, measure better to protect
AI is transforming software development. It speeds up actions, amplifies capabilities and modifies the distribution of work. But it also introduces a new complexity: that of permanent verification, information overload and trust in assisted systems.
In this context, organizations that simply measure speed will run the risk of confusing activity and performance.
True performance in the AI era isn’t just about delivering faster. It consists of delivering better, with less friction, more confidence and better protection of the teams’ cognitive capital.
DORA remains essential for measuring execution. SPACE becomes essential for measuring the sustainability of the human system.
The next frontier of Developer Experience will therefore not only be pipeline automation.
It will be the ability to design environments where AI accelerates without exhausting, assists without disorienting, and truly increases collective performance.
