In this closing panel from DX Annual, Rafe Colburn, Chief Product and Technology Officer at Etsy; Jesse Adametz, Senior Director of Engineering, Platform Engineering at Twilio; Eirini Kalliamvakou, Research Advisor at GitHub; Collin Green, Senior Staff UX Researcher at Google; and Brian Houck, Senior Principal Applied Scientist at Microsoft debate some of the biggest questions surrounding AI and engineering productivity.

They discuss whether AI will reduce the need for engineers, how AI is affecting technical debt, the future role of software engineers in an agentic world, and whether organizations should mandate AI adoption. They also explore how bottlenecks are shifting across the software development lifecycle, the challenges facing junior engineers, and why learning, culture, and change management may ultimately matter more than the tools themselves.

Some takeaways:

AI is changing software engineering, but not eliminating the need for engineers

• The panel largely rejected the idea that an AI-first SDLC means dramatically fewer engineers. As the cost of building software decreases, demand for software is likely to increase, creating new opportunities rather than eliminating the need for technical talent.

• Several panelists argued that the role of software engineers will evolve rather than disappear. The tasks that make up the job may change, but organizations will continue to need people who can solve problems, make decisions, and build systems.

Technical debt remains a tradeoff, not just an AI problem

• Panelists disagreed on whether AI is creating technical debt faster than it can remove it. Some argued that AI is accelerating both code generation and technical debt, while others believed the underlying business pressures that create technical debt remain largely unchanged.

• The discussion also introduced the idea of cognitive debt. As engineers rely more heavily on AI-generated code, understanding and maintaining systems may become more difficult even if development velocity increases.

The future engineer may work at a higher level of abstraction

• Several panelists predicted that engineers will spend less time writing code directly and more time defining intent, setting constraints, providing context, and validating results. Rather than replacing engineering work, AI may shift it to a different level of abstraction.

• The panel also pushed back on the idea that engineers will simply become managers of agents. Effective AI use still requires technical judgment, communication skills, and careful oversight.

Mandates rarely create meaningful AI adoption

• Most panelists opposed the idea that organizations should mandate AI usage. Instead, they emphasized enablement, reducing friction, and helping developers discover value through their own workflows.

• Usage metrics can easily become the wrong goal. The group cautioned against treating AI usage itself as a performance metric, arguing that outcomes matter more than activity.

Junior engineers remain essential to the future of the profession

• The panel strongly rejected the idea that organizations will no longer need junior engineers. Today’s junior engineers become tomorrow’s senior engineers, making talent development critical to the long-term health of the industry.

• Several speakers also noted that newer engineers may bring valuable AI-native perspectives. Just as previous technology shifts rewarded developers who grew up with new tools, the next generation may help shape how AI is used in practice.

The biggest AI adoption challenges are human, not technical

• While tooling matters, the panel repeatedly returned to learning, culture, incentives, and change management as the biggest barriers to successful AI adoption. Engineers are navigating rapid technological change, shifting workflows, and new expectations about their role.

• Organizations that create space for learning appear to see stronger results. The panel highlighted examples where teams learned together, experimented together, and achieved better adoption outcomes than individuals working in isolation.

In this episode, we cover:

(00:00) Intro

(01:16) Why an AI-first SDLC doesn’t mean fewer engineers

(03:09) The debate over AI and technical debt

(07:40) AI-generated code and the future role of engineers

(14:16) Why mandating AI use doesn’t necessarily lead to better outcomes

(20:43) Predictions for the future of junior engineers

(23:22) Where the bottlenecks are in the SDLC now

(28:25) How risk influences AI use

(32:38) Why the human side is the biggest AI adoption challenge

Referenced:

• Etsy

• GitHub

• Microsoft

• Twilio

• Google

• Stewart Reichling

• What is the SPACE framework and when should you use it?

Indeed increased AI coding tool adoption from roughly 25% to 97% across its engineering organization, but getting engineers to use the tools was only part of the challenge.

In this session from DX Annual, Michael Redding, Principal Product Manager, and Jeff Davis, VP of Core Infrastructure at Indeed, explain how the company used structured training, leadership support, and ongoing community engagement to help more than 2,000 engineers build practical AI skills. They share why an early train-the-trainer model fell short, how they redesigned their approach around hands-on learning, and what they learned about balancing adoption, measurement, and psychological safety.

They also discuss the impact of the program on coding time, the role of continuous enablement after formal training ended, and how Indeed is preparing for the next phase of AI adoption, including agentic workflows and AI-powered coaching.

Some takeaways:

Indeed started with a productivity problem, not an AI problem

• At the beginning of 2025, Indeed’s DX survey showed that only about half of developer time was being spent on new features and innovation. The remaining 48% was consumed by maintenance, upgrades, incident response, and other forms of engineering overhead.

• The company’s AI strategy focused on two goals: reducing overhead work and increasing output during coding time. The long-term objective was to double engineering productivity by shrinking non-value-added work while helping engineers produce more during the time they spend building.

AI Coding Essentials succeeded where AI Coding Ambassadors fell short

• Indeed’s first enablement effort, AI Coding Ambassadors, used a train-the-trainer model built around roughly 60 AI champions across the organization. While ambassadors maintained high levels of engagement, adoption among their teammates declined after the program ended.

• The company responded by launching AI Coding Essentials (AICE), a structured training program designed for all engineers. The experience convinced the team that direct, hands-on learning was far more effective than relying on knowledge to spread organically through teams.

Indeed treated AI upskilling as a company-wide investment

• Training more than 2,000 engineers required significant organizational commitment and leadership support. Michael estimated the investment at roughly $3–4 million in engineering time across the company.

• Rather than mandating AI usage, Indeed strongly encouraged completion of the training itself. Managers were given visibility into participation, while engineers retained flexibility in how and whether they ultimately incorporated AI into their workflows.

AI adoption increased from 25% to 97%

• Despite offering AI tools, training resources, and executive support, weekly AI usage remained stuck around 25% at the start of 2025. The challenge was not tool access but helping engineers develop practical skills and confidence.

• By the time of the presentation, weekly AI tool usage had reached approximately 97%. The company also successfully navigated multiple tool transitions, moving from Cody and Copilot to newer agentic tools such as Claude Code, Cursor, Windsurf, and Amp.

Structured training produced measurable results

• Engineers who completed AI Coding Essentials reduced coding time by roughly 35–36%, while engineers who did not complete the training saw little change. Across the broader organization, coding time decreased by roughly 20%.

• Indeed measured coding time as the period between a developer picking up a Jira ticket and opening a diff in GitLab. The company continued to see benefits months after training ended, especially as newer frontier models became available.

Community and continuous enablement kept momentum going

• Indeed reinforced learning through coding forums, office hours, hackathons, Slack communities, and its AI Showcase recognition program. More than 100 unique community posts were being shared monthly in the company’s primary AI channel.

• The goal was to make AI learning continuous rather than event-based. Engineers had multiple ways to share discoveries, get help, and learn from peers long after formal training concluded.

The next challenge is moving beyond coding

• Indeed is now focused on agentic workflows, AI coaching, and expanding enablement beyond software engineering. Product managers, designers, researchers, and other R&D functions are becoming part of the company’s AI adoption strategy.

• As coding becomes faster, bottlenecks are beginning to shift elsewhere in the development lifecycle. The team is already monitoring signs that code review and other downstream activities may become the next constraints on engineering throughput.

In this episode, we cover:

(00:00) Intro

(01:05) Indeed’s DX survey from January 2025

(02:30) The two-part strategy to double engineering productivity

(04:21) How Indeed increased AI adoption from 25% to 97%

(15:40) Results from Indeed’s AI training program

(18:33) How Indeed sustains AI adoption and learning

(23:06) What’s next for AI enablement at Indeed

(24:41) Q&A: How coding time was calculated

(25:25) Q&A: How Indeed uses AI playbooks

(26:40) Q&A: Balancing asynchronous and live AI training

(28:22) Q&A: Psychological safety during AI adoption

(31:44) Q&A: Why AI adoption spikes after the holidays

(33:20) Q&A: The metrics Indeed tracked

(35:22) Q&A: Where the time savings are going

(36:54) Q&A: Reaching engineers who skipped the training

(38:08) Closing thoughts

Referenced:

• Indeed

• Claude Code | Anthropic’s agentic coding system

• Cursor

• Windsurf

• Amp Code

• The Complete Guide to Building Skills for Claude | Anthropic

• Measuring developer productivity with the DX Core 4

In this session from DX Annual, Uma Namasivayam, Senior Director of Engineering Productivity at Dropbox, shares how the company’s developer productivity efforts evolved from improving developer experience to preparing for the agentic era.

He explains how Dropbox approached AI adoption across its engineering organization, the impact it had on developer productivity, and why faster code generation is creating new bottlenecks in areas such as code review, validation, and CI/CD. He also discusses Dropbox’s efforts to rethink engineering systems, measurement, and workflows, including the development of agentic tooling and new metrics designed to move beyond PR throughput and toward product velocity.

Some takeaways:

Dropbox’s productivity journey started before AI

• DXI helped Dropbox identify productivity problems as system problems rather than talent problems. When the company began measuring developer experience in 2023, it found significant variation across teams in DXI scores, PR throughput, and cycle time.

• Measuring developer experience created a framework for prioritizing investments. The team used DXI to identify friction across areas such as debugging, documentation, and build systems while giving leadership a common language for discussing productivity.

AI adoption required more than access to tools

• Dropbox combined executive support, developer segmentation, enablement, and strong guardrails to drive adoption. Different teams and developer roles were matched with different tools and workflows based on their needs.

• The approach helped Dropbox increase AI adoption from roughly 30% to 100% within six months. During the same period, PR throughput doubled and developer satisfaction with AI tools increased significantly.

Engineers used their extra capacity to tackle neglected work

• As AI increased throughput, engineers naturally pulled maintenance work, migrations, and technical debt from the backlog. Dropbox saw significant growth in these categories without any specific direction from leadership.

• The additional capacity was often reinvested into engineering health. Teams used the opportunity to address long-standing issues that had accumulated over time rather than focusing exclusively on new feature development.

The next challenges are scale, trust, and measurement

• Dropbox believes the move to agentic engineering creates three major challenges: scale, validation and trust, and measurement. Existing development systems were not designed for a world where AI dramatically increases code throughput.

• As code generation accelerates, bottlenecks are shifting toward code review, validation, and CI/CD systems. The company is already seeing pressure move downstream in the software development lifecycle.

Agentic engineering requires redesigning the entire system

• Uma compared the transition to the shift from steam-powered factories to electric factories. The biggest gains came from redesigning the entire system rather than simply replacing one technology with another.

• Dropbox is investing in agentic workflows across the SDLC and building Nova as an orchestration layer. The company is evaluating roughly 30 development steps, and one in twelve pull requests is already being generated by Nova.

PR throughput is becoming a less useful measure of productivity

• Dropbox believes traditional engineering metrics need to evolve alongside AI. As agentic workflows become more common, measuring productivity through pull request volume alone provides an incomplete picture of engineering output.

• The company is increasingly focused on metrics such as AI contribution, loaded cost per PR, agentic workflow coverage, work distribution, and time to ship. The goal is to better connect engineering activity to customer value and business outcomes.

In this episode, we cover:

(00:00) Intro

(00:57) The beginning of Dropbox’s DX journey

(02:34) AI adoption at Dropbox: what made it work

(04:46) The results of Dropbox’s AI adoption efforts

(05:39) What the results mean for the business

(06:55) The phases of AI adoption and where they are now

(08:00) The new bottlenecks

(09:16) Three challenges Dropbox faces moving into agentic engineering

(10:05) How Dropbox is redesigning the SDLC for agentic engineering

(15:46) The new metrics that matter

(19:16) Final takeaways

Referenced:

• Dropbox

• Developer Experience Index (DXI) | DX

• DX Core 4 Productivity Framework

• Cursor

• Claude Code | Anthropic’s agentic coding system

• JetBrains

• Visual Studio Code

• Jira | Project Management for the AI Era | Atlassian

• GitHub

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When AI coding tools started delivering meaningful results, a predictable question followed from CTOs and engineering leaders: how do we measure the impact? There is a strong instinct to assume that the frameworks built over the last decade no longer apply, and that the age of AI demands a fundamentally different measurement architecture.

I’d push back on that instinct. The evidence suggests the opposite is closer to the truth.

While AI represents a massive paradigm shift in how software is built, it does not alter what engineering organizations are ultimately trying to accomplish. Foundational engineering principles still map to high-level outcomes. How quickly is value delivered? How easy is it for developers to do their work effectively? How stable are the systems? And, ultimately, what is the business impact of the work? Rather than rendering these categories obsolete, the introduction of AI makes anchoring to a stable, outcome-oriented framework more critical than ever.

Engineering leaders are under unprecedented pressure to justify the massive budgets being poured into AI tooling. When executives demand proof that an AI investment is paying off, the immediate temptation is to reach for a shiny new metric that isolates the tool itself. But that is exactly where the risk lies.

The DX Core 4 framework (speed, effectiveness, quality, and business impact) is built around answering these persistent questions. It was designed to give engineering leaders a durable measurement architecture that survives new technology cycles. AI is a significant shift in workflow, but because the framework anchors to macro outcomes rather than the mechanics of coding, it remains stable. If anything, the rise of AI makes this type of durable framework more important, not less.

This article makes three related arguments:

1. First, the high-level dimensions of engineering productivity remain remarkably stable, even as AI transforms how software is built.

2. Second, AI-specific telemetry should be treated as diagnostic context rather than a replacement for outcome-oriented measurement.

3. Finally, while many traditional engineering metrics remain valuable, the behaviors that generate them are changing, and disentangling those signals requires triangulating across the layered structure of diagnostic, system, and outcome metrics.

The Core 4 holds (and here’s why that matters)

The value of anchoring to these four overarching dimensions—speed, effectiveness, quality, and business impact—is that they synthesize key principles from DORA, SPACE, and DevEx into a unified methodology. Core 4 inherits DORA’s focus on delivery outcomes, SPACE’s insistence that productivity is multidimensional, and DevEx’s emphasis on the lived experience of developers—and combines them into a four-dimension framework optimized for executive decision-making.

AI doesn’t change what engineering organizations are trying to accomplish. What it does is make the signals noisier.

As AI coding assistants become standard and agentic workflows begin handling multi-step tasks autonomously, traditional activity metrics shift in ways that can easily mislead. Pull request counts spike, cycle times compress, and code volumes bloat. Engineering leaders who chase these surface-level fluctuations without anchoring to a balanced, outcome-oriented framework risk optimizing for sheer motion rather than actual progress.

This is precisely where a high-level outcome framework proves its utility. I’m using the Core 4 as the specific example here, but the same logic applies to any mature measurement framework aligned to the principles of SPACE. By focusing on outcomes that matter, regardless of how code gets written, the model remains insulated from technology disruptions. This structural design looks increasingly necessary as developer workflows continue to evolve away from manual synthesis and toward intent-driven architecture.

Activity vs. outcome: The role of AI telemetry

To be clear, focusing on measuring stable macro outcomes does not mean engineering leaders should ignore AI adoption and usage. Tracking how developers engage with AI tools is incredibly valuable, but it is critical to understand what those metrics are telling us.

AI adoption, token usage, and the number of tasks assigned to agents are examples of diagnostic telemetry. Like more traditional operational metrics such as pull request size, build duration, or meeting load, they provide visibility into how work is being performed rather than whether it is producing better outcomes.

One way to think about this distinction is illustrated in the image below, whether AI-specific or traditional, helps explain the mechanics of software delivery and the dynamics of the engineering system. By contrast, outcome-oriented frameworks evaluate whether those operating patterns are ultimately translating into better engineering results.

Specialized measurement frameworks can help organize these diagnostic signals. For example, DX’s AI Measurement Framework combines AI-specific telemetry around utilization and cost with outcome-oriented metrics to evaluate AI’s overall impact on engineering organizations. These two classes of measurement answer fundamentally different questions: “How is work being performed?” versus “Is the engineering organization delivering better outcomes?”

The value of tracking AI activity is that it helps us understand the shifting patterns that lead to our outcomes. For example, if a team’s AI adoption spikes to 90%, that metric alone doesn’t prove success. Instead, it serves as a lens to interpret changes in the Core 4: did that spike in adoption correlate with an increase in speed? Did it negatively impact quality via a higher change failure rate? Or did it inadvertently degrade developer effectiveness by introducing new code-review bottlenecks?

Tracking AI telemetry tells us how the work is changing. Tracking the core dimensions tells us if that change is actually delivering results.

When leaders are tasked with proving AI investment ROI, they cannot do it by pointing to adoption spikes or token volume. A high utilization rate means nothing if software delivery stalls or system stability crashes. Outcome-based developer experience metrics aren’t just a way to measure engineering anymore, they may be the most reliable ledger for proving AI value.

PR throughput in the AI era

Of the key metrics within the Core 4, PR throughput has attracted the most debate, both before and after the arrival of AI.

The criticism of PR throughput is entirely fair at the individual level. Not all PRs are created equal in terms of size, complexity, or value. DX developed a methodology called TrueThroughput, which uses AI to normalize these variations by weighting PRs based on actual complexity. Yet, even with that kind of normalization in place, the metric is a poor instrument for evaluating any individual developer’s contribution. I’ve argued this myself, and I’d stand by it. Using PR throughput to assess individuals is the wrong application of the metric.

At the system level, though, it remains one of the most useful signals available. The reason is that it doesn’t just measure output, it measures engineering flow. Whether code in a pull request was written by a human or generated by an AI agent, if it’s moving through review, CI, and deployment without friction, the metric reflects that. If it’s stalling—because review is bottlenecked, builds are flaky, or deployment processes are slow—the metric surfaces that too. PR throughput is a signal for whether an engineering system can move work through, regardless of where that work originates.

It also occupies a unique position among the Core 4 metrics. Unlike measures such as Change Failure Rate or DXI, which continue to evaluate enduring organizational outcomes, PR throughput is directly tied to the mechanics of software delivery. As workflows evolve from code-first to intent-first development, the role of the pull request itself may change substantially, making PR throughput more susceptible to reinterpretation than most other metrics in the framework.

In our own longitudinal research at DX, we found that AI coding tools produced roughly a 7.8% increase in PR throughput across organizations that had adopted them. That’s a real and meaningful signal. It’s also a useful corrective to more optimistic claims about AI’s productivity impact. The gains are real; they tend to be more modest than headline figures suggest, and they vary considerably across different types of work.

The majority of code shipped in production is still written by humans, though that share is shifting. Our research showed that during the first quarter of 2026, the percentage of code generated by AI that reaches production is 27.4% of production code on average. For most engineering organizations today, pull requests remain the primary unit of software delivery, making PR throughput one of the clearest indicators of engineering system flow.

If, and when, the transition to intent-first workflows materializes, the field will likely need a metric that captures innovation velocity as a higher level of abstraction. The Idea-to-Customer velocity metric introduced in the recent EngThrive framework paper is one implementation worth watching as a future key metric for the speed dimension. But even in that future, PR throughput will likely remain a crucial secondary metric for diagnosing system flow.

Evolving the interpretation, not the framework

To recap, the top-level dimensions of the DX Core 4 are stable and as meaningful as ever. The key metrics that support them also continue to hold.

What is changing is the diagnostic layer beneath them, the operational signals that have always helped explain how engineering systems produce those outcomes. AI doesn’t change what good looks like at the outcome level, but it does change the mechanisms that generate many of our familiar diagnostic metrics. The same number can now be produced by very different combinations of human and AI behavior, which means individual diagnostic metrics are noisier than they used to be, and the signals they do provide may relate to outcomes in different ways than they used to.

Take, for example:

• PR Merge Rate: Historically, a high merge rate signaled a highly aligned team shipping clean, uncontroversial work. In an agentic workflow, does a 95% merge rate mean the AI is flawless? Or does it mean your human developers are rubber-stamping machine-generated code because they’re too overwhelmed to properly review it?

• Time-to-10th-PR: This is currently one of my favorite onboarding metrics because it is highly predictive of a new hire’s long-term success and speed-to-productivity. But its utility faces an unresolved question: if an AI onboarding assistant can help an engineer generate and ship 10 PRs by their second afternoon, does that metric still capture true structural onboarding health? Or does it just track how quickly someone learned to use AI tools?

This is the core challenge. The data points themselves have not changed, but the behaviors that generate them have. AI activity metrics, such as tool adoption or token counts, provide critical context for understanding why traditional engineering metrics move the way they do, but they do not replace those metrics.

Triangulating between diagnostic metrics, engineering system metrics, and high-level outcome metrics is what lets us translate how teams work into whether they’re achieving what they set out to. Building a map of these new patterns—how to interpret them, and what outcomes they predict—will be critical work for engineering teams and researchers moving forward.

Final thoughts

The instinct to reach for entirely new metrics in this age of AI is understandable. AI is genuinely reshaping how software gets built, and it is reasonable to question whether existing measurement frameworks can keep pace.

But our research and data show that the core dimensions of productivity have held up, not because they anticipated AI specifically, but because they were designed around enduring organizational outcomes rather than any particular workflow or technology. Speed, effectiveness, quality, and business impact remain the right questions to ask, whether code is written by a developer at a terminal or generated by an autonomous agent.

What has changed is not what we should measure, but how we should interpret it. AI-specific telemetry provides valuable diagnostic context for understanding how work is evolving, but it does not replace outcome-oriented measurement. Likewise, familiar engineering metrics such as PR throughput, merge rates, or onboarding velocity continue to provide meaningful signals, even as the behaviors that generate those signals shift.

The priority for engineering leaders is not to rebuild their measurement architecture from scratch. It is to learn to interpret existing frameworks through a new lens, one that recognizes the growing role of AI while remaining anchored to the outcomes that ultimately matter.

The framework is stable. The interpretation is where the real work begins.

That’s it for this week. Thanks for reading.

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In this session from DX Annual, Christopher Sanson, Product Lead, AI Developer Experience, and Madison Capps, Engineering Manager, Infrastructure at Airbnb, challenge some of the most common assumptions about AI. Is AI primarily about replacing humans? Do organizations need mandates to drive adoption? And are the productivity gains really as small as some studies suggest?

Using examples from Airbnb’s own AI journey, they share how the company achieved widespread adoption of agentic AI through AirChat, community enablement, and internal tooling rather than top-down mandates. They also discuss the impact AI is having on developer productivity, how non-developers are increasingly using coding tools, and how teams are rethinking product development in an AI-first world.

Finally, Madison takes a deeper look at the infrastructure powering Airbnb’s AI strategy, including AirChat CLI, the AirChat SDK, and AirChat Remote, along with the company’s vision for asynchronous agent workflows and the next generation of AI-powered development.

Some takeaways:

AI adoption at scale

• Successful AI adoption does not require mandates. Airbnb achieved 97% weekly usage and 90% daily usage of agentic AI tools among engineers without tying adoption to performance reviews or quotas. Christopher argued that the best adoption comes when developers choose to use AI because it genuinely helps them work faster and better.

• Treat internal AI tools like products, not internal infrastructure. Airbnb built a recognizable brand around AirChat, invested in onboarding and workshops, created internal marketing materials, and focused heavily on user experience. That product mindset helped turn AirChat into a company-wide platform rather than just another engineering tool.

• Community-driven learning scales better than centralized training. AI champions, train-the-trainer programs, hackathons, workshops, and active peer-to-peer learning channels allowed knowledge to spread organically across the company. Over time, the AI community became larger and more active than the team managing the platform itself.

Productivity gains are accelerating

• Developers are spending more time actively coding. Christopher challenged the idea that engineers only spend a small percentage of their time writing code. As coding becomes faster and easier with agentic AI, developers can spend more of their week building software rather than working around implementation bottlenecks.

• The most active AI users see the largest productivity gains. Airbnb found that developers who spent four or more hours per day working with agentic AI dramatically increased their output. The relationship between AI usage and productivity became stronger as engineers learned how to incorporate agents into their daily workflows.

• PR throughput increased by 65% after the introduction of agentic AI. Airbnb’s data suggests that productivity gains extend well beyond the single-digit improvements often cited in industry studies. Developers who heavily embraced agentic AI moved from industry-average output to some of the highest throughput levels measured internally.

• AI-authored code is becoming mainstream. Roughly 59% of Airbnb’s code is now primarily authored by AI, and more than half of developers report that AI generates the majority of the code they work with. Christopher argued that this shift is happening far faster than most organizations realize.

AI is spreading beyond engineering

• The addressable market for AI is much larger than developers alone. Airbnb initially expected adoption to level off around its engineering population. Instead, usage continued growing as product managers, designers, finance teams, and operations teams began integrating agentic AI into their work.

• People will learn new workflows when the value is obvious. Some non-engineering teams adopted VS Code and terminal-based tools simply because they provided the best access to agentic AI capabilities. Rather than resisting technical tools, employees were willing to learn them in exchange for meaningful productivity gains.

• Domain experts are increasingly building their own AI-powered solutions. Airbnb’s internal platforms allow teams to create specialized applications tailored to their own workflows. This shifts more problem-solving into the hands of the people closest to the business problem.

Rethinking how work gets done

• Many existing processes were designed around expensive software development. Product reviews, lengthy requirements documents, and sequential handoffs evolved in a world where implementation was slow and costly. AI changes those economics and creates opportunities to redesign workflows from first principles.

• AI enables faster movement from ideas to prototypes. Rather than spending weeks refining specifications before building anything, teams can generate multiple prototypes quickly, test ideas earlier, and iterate before committing significant resources.

• Smaller teams can collaborate earlier and move faster. Airbnb sees opportunities to reduce handoffs between product managers, designers, and engineers by bringing teams together earlier in the process and using AI to accelerate exploration and execution.

Building for asynchronous AI

• Current agentic AI tooling still creates friction. Managing multiple sessions, handling long-running tasks, maintaining context, and switching between workflows remain cumbersome despite major advances in model capabilities.

• The next frontier is asynchronous agent workflows. Rather than interacting with a single agent in real time, developers are increasingly orchestrating multiple agents working in parallel, often across long-running tasks that continue without constant supervision.

• Airbnb is investing in infrastructure, not just models. AirChat CLI, migration tooling, the AirChat SDK, and AirChat Remote were all built around the belief that future gains will come from workflow orchestration, platform capabilities, and developer experience as much as from improvements in foundation models.

Preparing for an AI-first future

• Organizations should build for where developer workflows are heading. Madison described Airbnb’s approach as continuously forecasting how engineers are likely to work in the near future and investing in the infrastructure required to support those workflows before they become mainstream.

• AI-first architecture will become increasingly important. As throughput rises and more work is delegated to agents, teams will need stronger guardrails, scalable platforms, and systems designed specifically to support AI-assisted development.

• The biggest bottlenecks are shifting away from code generation. As AI reduces implementation costs, constraints move elsewhere in the system. Coordination, validation, infrastructure, and workflow management are becoming the new challenges organizations must solve.

In this episode, we cover:

(00:00) Intro

(01:37) Myth #1: AI is about replacing humans

(03:22) Myth #2: You need mandates to drive AI adoption

(05:21) AirChat, agentic AI, and Airbnb’s adoption strategy

(08:07) Myth #3: AI has little impact on productivity

(09:33) Airbnb’s increase in coding time and PR throughput

(14:20) Myth #4: AI coding tools are just for coders

(15:39) How non-developers are using coding tools

(17:24) Rethinking product development in an AI-first world

(20:30) Myth #5: Vibe coding isn’t coding

(22:16) Unsolved problems in agentic AI tooling and how Airbnb is addressing them

(26:30) Airbnb’s overall AI philosophy in practice

(29:15) Using agentic AI to accelerate code migrations

(30:18) AirChat SDK: How Airbnb enables teams to build AI-powered applications

(33:17) AirChat Remote and asynchronous agent workflows

(36:07) Predictions for what’s next

Where to find Christopher Sanson:

• LinkedIn: https://www.linkedin.com/in/christophersanson

Where to find Madison Capps:

• LinkedIn: https://www.linkedin.com/in/madison-capps-66950625

Referenced:

• Airbnb

• Steve Jobs’s Bicycles for the Mind

• Jennifer St Pierre

• Justin Reock

• AI-generated merged code holds steady at ~30%

• Andrej Karpathy’s post on X

In this session from DX Annual, Rebecca Fitzhugh, Lead Principal Engineer at Atlassian, moderates a panel featuring Nidhi Allipuram, Vice President, Enterprise Developer Experience and Platform at Nationwide, Jai Schniepp, Senior Director, DevX Product Management at Comcast, Brent Foster, Vice President and Head of Architecture and Strategy at TD Bank, and Praveena Patchipulusu, Vice President of Engineering at HPE.

Together, they discuss how large enterprises are approaching AI adoption, what it takes to build an AI-first software development lifecycle, and how engineering leaders are balancing speed, security, governance, and developer experience. They also share their perspectives on the changing role of engineers, human accountability, and how organizations can prepare for the future of software engineering.

Some takeaways:

Building an AI-first software development lifecycle

• AI adoption is becoming a redesign effort, not a tooling effort. Several panelists argued that the biggest opportunity is not simply adding AI assistants to existing workflows but rethinking the software development lifecycle itself. Rather than treating AI as a coding tool, organizations are beginning to integrate it into requirements gathering, design, testing, code reviews, and deployment.

• Training and organizational support matter more than tool selection. Nationwide found that productivity gains came less from introducing new tools and more from providing engineers with training, coaching, playbooks, and time to learn. Teams consistently reported that air cover, psychological safety, and opportunities to experiment were more valuable than access to additional AI products.

• Successful adoption requires systems, not mandates. Organizations cannot simply tell teams to “go use AI.” Several panelists described building AI champion programs, governance models, embedded coaching, and structured learning opportunities that help teams develop new habits and scale adoption across large enterprises.

Keeping humans accountable

• Humans remain responsible for outcomes regardless of who writes the code. Every panelist emphasized that accountability does not shift to AI. Whether code is generated by an engineer, a copilot, or an agent, humans remain responsible for validating outputs, making decisions, and owning the results delivered to customers.

• Validation is becoming more important than approval. Traditional approval processes may matter less than ensuring the right people validate assumptions, outcomes, and risks. Teams are increasingly focused on creating workflows where humans review and challenge AI-generated work rather than simply acting as signoff gates.

• Decision-making is becoming a core engineering skill. As AI takes over more implementation work, engineers are spending more time evaluating tradeoffs, validating outputs, and making judgment calls. The ability to make good decisions quickly may become a larger differentiator than the ability to manually write code.

Security and governance in an AI-powered world

• Shift-left practices become even more important with AI. Security, compliance, and quality checks are being pushed earlier into the development process. Rather than relying on reviews at the end of the pipeline, organizations are embedding guardrails directly into workflows and development platforms.

• AI-generated infrastructure introduces new challenges. The conversation extended beyond application code to infrastructure. As AI increasingly generates Terraform, YAML, and cloud configuration files, organizations must build policy-driven validation and security controls to prevent vulnerabilities from entering production environments.

• Context is both a powerful asset and a potential risk. One of AI’s greatest strengths is its ability to use organizational knowledge and historical context. At the same time, exposing that information to AI systems creates new security concerns, making governance and access controls increasingly important.

The changing role of the engineer

• Engineers are becoming orchestrators rather than implementers. As AI takes over more boilerplate work, engineers are expected to focus more on system design, architecture, critical thinking, and coordinating work across humans, agents, and platforms. Success increasingly depends on defining intent and evaluating outcomes rather than writing every line of code manually.

• Role boundaries are becoming less rigid. The panel described a future where engineers, product managers, designers, and other builders work more closely together. AI is making it easier for individuals to contribute across traditional functional boundaries, creating smaller teams with broader responsibilities.

• Critical thinking and creativity become more valuable. While AI can accelerate execution, it cannot replace human judgment and problem framing. Several panelists argued that creativity, curiosity, and the ability to think differently about problems will become increasingly important as AI capabilities continue to improve.

Rethinking developer experience

• Developer experience is becoming workflow experience. The focus is shifting from individual tools toward creating trusted workflows that help teams move from idea to production more quickly. Organizations are increasingly measuring success by how effectively teams can deliver outcomes rather than by how efficiently they write code.

• Developer experience now includes agent experience. As AI agents become active participants in software delivery, organizations must consider how agents consume context, operate within guardrails, and interact with development platforms. Designing effective systems now means thinking about both human and AI users.

• Breaking down silos creates better outcomes. Several panelists argued that AI provides an opportunity to reduce friction between product managers, designers, developers, and security teams. The organizations that benefit most may be those that remove barriers between disciplines and enable more collaborative ways of working.

Preparing for the future

• The time to experiment is now. Every panelist encouraged organizations to begin learning through direct experience rather than waiting for the technology to mature. Teams that develop AI skills, workflows, and governance practices today will be better positioned as the technology continues to evolve.

• Institutional knowledge may become a competitive advantage. Large enterprises possess decades of documentation, decisions, diagrams, and expertise that often remain difficult to access. Several speakers highlighted the opportunity to unlock that knowledge and make it useful through AI-powered systems.

• Fundamentals still matter. Despite rapid technological change, the panel repeatedly returned to the same conclusion: strong engineering fundamentals, sound judgment, accountability, security practices, and critical thinking remain essential regardless of how much AI enters the software development process.

In this episode, we cover:

(00:00) Intro

(02:28) The AI journey across TD Bank, Comcast, and HPE

(05:59) Inside Nationwide’s AI-assisted development lifecycle

(10:04) Reimagining the software development lifecycle with AI

(11:32) Security, governance, and human accountability

(15:27) Embedding security and guardrails into AI workflows

(17:55) How AI is changing the role of an engineer

(21:52) What developer experience looks like in the AI era

(26:55) What software engineering may look like in 2030

(32:47) How to prepare for the AI-driven future

Where to find Rebecca Fitzhugh:

• LinkedIn: https://www.linkedin.com/in/rmfitzhugh

• X: https://x.com/RebeccaFitzhugh

Where to find Jai Schniepp:

• LinkedIn: https://www.linkedin.com/in/jessicaschniepp

Where to find Nidhi Allipuram:

• LinkedIn: https://www.linkedin.com/in/nidhi-allipuram

Where to find Brent Foster:

• LinkedIn: https://www.linkedin.com/in/engineeringthefuture

• Website: https://brentfoster.me

Where to find Praveena Patchipulusu:

• LinkedIn: https://www.linkedin.com/in/praveena-patchipulusu-158741

Referenced:

• Atlassian

• TD Bank

• Comcast Corporation

• Hewlett Packard Enterprise (HPE)

• Nationwide

• GitHub Spec Kit

• Abi Noda

As AI becomes embedded in software development, many of the metrics that engineering organizations have relied on for years are starting to break down.

In this session from DX Annual, Uber’s Ty Smith and Abhishek Tibrewal share how their approach to measuring AI’s impact on developer productivity has evolved over time. They walk through the different phases of their measurement journey, from adoption and engagement to measuring impact, ROI, and agentic value, explaining what they chose to measure at each stage, what worked, what failed, and how their thinking changed along the way.

They also discuss the role of qualitative feedback before telemetry existed, the challenge of identifying meaningful engagement signals, why “developer years saved” failed as an ROI metric, and how AI agents forced them to rethink traditional productivity measurements. Finally, they introduce Uber’s emerging framework built around feature velocity and explore the unanswered questions that remain as software development becomes increasingly agent-driven.

Some takeaways:

Why AI breaks traditional productivity metrics

• Many measurement frameworks were built for a world where humans wrote most of the code. As AI agents become more capable, metrics that once provided useful signals can quickly become misleading.

• Teams should expect their metrics to break. Uber’s measurement journey required repeatedly revisiting assumptions as AI-assisted development evolved into agentic workflows.

Start with stakeholder questions

• The best metrics answer real business questions. Uber worked backward from questions about productivity, ROI, investment priorities, and business value instead of collecting data for its own sake.

• Measurement should support decision-making. Metrics influence budgets, tooling investments, enablement efforts, and long-term strategy.

• Use qualitative signals before telemetry exists

• Qualitative feedback can be the fastest path to insight. Before AI tooling generated reliable telemetry, Uber relied on surveys, interviews, and experience sampling to understand adoption and guide investments.

• Behavioral questions are more useful than perception questions. Asking developers what they actually did produced stronger signals than asking whether they found AI helpful.

• Measure engagement through behavior, not demographics

• Behavioral patterns revealed insights that demographics could not. Role, tenure, and organization offered limited signal compared to how engineers actually used AI tools.

• A small group of AI power users emerged early. Studying usage patterns helped Uber identify engineers who were engaging deeply with AI and generating outsized results.

Correlation is not causation

• High AI usage does not automatically prove AI caused higher productivity. The most productive engineers are often the first to adopt new tools.

• Rigorous analysis matters when making investment decisions. Uber used causal methods to better understand the true impact of AI-assisted development.

Why measuring AI ROI is difficult

• Developer years saved sounded compelling but failed as an ROI metric. The approach created anxiety around replacement, required constant recalibration, and did not answer the business questions leadership cared about most.

• Business leaders ultimately care about outcomes. Time saved is useful context, but value creation, customer impact, and business results matter more.

• PRs measure activity, features measure value

• Agentic AI exposes the limitations of activity-based metrics. A single agent task can generate many pull requests without creating meaningful customer value.

• Feature velocity became Uber’s new North Star. The goal shifted from measuring engineering output to measuring whether valuable capabilities were actually being delivered.

Building an AI-native measurement framework

• Feature velocity works alongside supporting metrics. Flow efficiency, quality, and capability expansion help create a more complete picture of AI’s impact.

• PR classification provides important context. Understanding the type and complexity of work helps distinguish meaningful progress from routine maintenance and toil.

• The future belongs to outcome-based metrics

• The most durable metrics are tied to business outcomes rather than engineering activity. As AI becomes more autonomous, output alone becomes a less reliable signal.

• Many important questions remain unanswered. Organizations still need better ways to measure judgment, autonomy, technical debt, and the value created by increasingly agent-driven software development.

In this episode, we cover:

(00:00) Intro

(01:30) Steve Yegge’s 8 stages of AI-assisted development

(03:22) Uber’s shift to a generative AI-powered company

(04:20) Uber’s pre-AI productivity metrics

(06:55) Important questions from stakeholders that previous metrics didn’t answer

(08:25) How Uber measures AI before telemetry exists

(11:11) Metrics used to measure adoption

(12:49) Measuring engagement

(14:30) Measuring impact

(16:32) The challenge of measuring AI ROI

(19:32) Rethinking adoption, engagement, and impact for agentic AI

(26:01) The new north star: Feature velocity

(28:41) PR classification + feature velocity: the questions it can answer

(33:01) What comes next and what’s still unanswered

(34:30) Lessons learned and what they’d do differently

(37:11) Q&A #1: How Uber defines a feature

(38:50) Q&A #2: Measuring success and AI ROI

Where to find Abhishek Tibrewal

• LinkedIn: https://www.linkedin.com/in/aabhishektibrewal

Where to find Ty Smith:

• LinkedIn: https://www.linkedin.com/in/tyvsmith

Referenced:

• Welcome to Gas Town

• Dara Khosrowshahi (Uber CEO)

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🗓 Join Brian Houck and me, this Thursday June 18th for a research briefing on measuring AI agents, revisiting the Core 4, and more. Register here.

In our Q1 AI impact analysis, we found that 27.4% of code was AI-authored. Because the space is changing quickly, the DX Research team reports on this metric quarterly to track changes in AI’s impact on organizations’ ability to create code.

To measure the change in AI-authored code, and the impact on quality, we conducted two analyses:

1. First we measured the percentage of AI-authored code, using self-reported data from developers. We define the metric as code generated by AI without major human rewrites.

   1. As with any self-reported metric, there is potential for bias in both directions—undercounting from fully autonomous workflows and overcounting when developers treat AI use as a performance signal. In the future we’ll share what we’re seeing from DX’s AI Code Insights, which automatically measures the percentage of AI-generated code.

   2. Our sample included DX data from over 400 companies from Q2 (April 2026-June 2026), reported by the average of user responses within each company. We interpret this data as estimates of the proportion of coding workload delegated to AI tools, rather than literal measures of code output. This reflects the assumption that respondents anchor to how often they ask AI to do work rather than measuring how much code AI actually produces.

2. Additionally, to evaluate the downstream impact of AI-authored code, we also looked at PR size using telemetry data from the same cohort over the last year (July 2025-June 2026).

   1. In the future we’ll share further investigations on the impact of increased AI-authored code, as well as the impact of increased PR size.

Here’s what we’re seeing.

AI-authored code is consistent across organization sizes

Our preliminary Q2 findings show that, on average, 51.9% of code is now AI-authored. Newer models, better workflow integration due in large part to usage of CLI tools, AI mandates, and learning curve progress have all contributed to this massive shift. While this indicates that AI is significantly impacting our ability to create code, it says little about the quality of the code being generated.

When segmented by organization size, our finding still holds. The median percentage of code that is AI-authored holds steady at around 50%. This reflects a broader shift in how code is produced, regardless of team size.

Median pull request size has nearly doubled

Because of the dramatic change in AI-authored code, we also looked at whether PR size—one measure for quality—has changed for the same cohort of companies over the past year. Interestingly, our data is showing an equally dramatic change: median PR size nearly doubled, growing from 44 lines to 72 lines per pull request between July 2025 and June 2026.

This finding confirms what many teams would expect: AI tends to generate more lines of code than humans. When the majority of code is machine-produced, that verbosity results in larger pull requests.

More broadly, this metric is becoming one of the most important to watch. Generally, more code can equal more complexity, less portability, and a greater potential for bugs and vulnerabilities. More verbose code can also be more difficult to review and maintain. One of the traits of a skilled engineer is the ability to fully implement a use case with exactly as much code as needed to perform the task. When AI undermines that instinct at scale, the result is not just technical debt. It is compounding cognitive debt across the team as engineers struggle to understand code they did not write.

The critical question for leaders: have review and quality processes kept pace with this volume? There’s been a lot of discussion in engineering leadership communities about how to shift processes to handle code review being the new bottleneck. I also appreciated Camille Fournier’s recent piece sharing guidelines for respectable use of AI, which outlines expectations leaders can set with their teams for using AI. If this is something you’re actively thinking about, please let me know in the comments—I’d love to hear from you.

That’s it for this week. Thanks for reading.

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Michael Galloway leads Platform Engineering at Mercari, while Snehal Shinde leads Cost and Performance Engineering. Together, they have been at the center of Mercari’s effort to become an AI-native company.

In this session from DX Annual, Michael and Snehal share what happened after Mercari’s CEO mandated 100% AI adoption across the organization. While AI accelerated code generation and increased engineering output, the team quickly discovered that their existing dashboards could not answer a simple question: was AI actually improving productivity?

They discuss how Mercari built new visibility into AI usage and software delivery, the bottlenecks they uncovered across the SDLC, why faster coding did not automatically translate into faster delivery, and the lessons they learned rolling out AI at scale. They also share how Mercari is rethinking software development around agents, feedback loops, and new ways of working.

Some takeaways:

Measuring AI impact

• AI adoption alone does not guarantee business value. Mercari found that while AI usage increased rapidly across the organization, existing dashboards could not answer the leadership team’s most important question: whether AI was actually improving productivity.

• Local optimization does not necessarily improve system-wide performance. Engineers reported working faster with AI tools, but end-to-end delivery metrics remained largely unchanged because bottlenecks elsewhere in the software delivery process continued to slow teams down.

• Organizations need visibility into both AI usage and delivery outcomes. Mercari built new dashboards that combined AI tool data with SDLC metrics to better understand adoption, throughput, quality, and operational performance.

The reality of becoming AI-Native

• AI adoption required a cultural transformation, not just a tooling rollout. Mercari’s CEO mandated company-wide AI adoption, but success depended on changing workflows, habits, and expectations across engineering, finance, legal, customer support, and other functions.

• Different teams required different forms of enablement. Employees varied significantly in their technical backgrounds and familiarity with AI tools, making education, workshops, and support systems essential to driving adoption.

• The goal was to rethink work itself. Rather than layering AI onto existing processes, Mercari challenged teams to reconsider what they built, how they built it, and how people worked together.

The bottlenecks AI exposed

• AI revealed problems that already existed inside the organization. Review queues, CI instability, deployment friction, and support requests became more visible as coding accelerated.

• Code generation was not the primary constraint. Engineers often spent more time waiting for approvals, navigating organizational boundaries, and dealing with infrastructure limitations than writing code.

• System complexity amplified AI-related challenges. As AI-generated changes increased, existing architectural complexity and fragile workflows became harder to ignore.

Finding AI workflow opportunities

• Mercari mapped AI opportunities across 33 domains. The AI task force reviewed functional areas across the company to identify where AI could automate work, where it could not, and where the strongest leverage points existed.

• The biggest opportunities extended far beyond engineering. Role-specific workshops helped teams in finance, legal, design, operations, customer support, and other departments find practical AI use cases in their own workflows.

• Early wins created proof points for broader adoption. Mercari saw measurable impact from support bots, accounting workflows, platform support automation, and Socrates, an internal BI agent that made company data easier to query and use.

Rethinking software development

• Faster coding shifted attention upstream. As implementation became easier, planning, specification, and decision-making emerged as larger constraints on delivery speed.

• Agent Spec-Driven Development moves AI earlier in the lifecycle. Mercari began using agents to analyze documentation, code, and organizational knowledge before implementation work started.

• Future workflows will focus more on intent than execution. Teams increasingly define goals, constraints, and success criteria while agents handle larger portions of implementation and validation.

Preparing for an agent-driven future

• Feedback loops matter more than ever. Mercari’s multi-loop SDLC emphasizes rapid validation, iterative learning, and increasingly autonomous agent workflows.

• Behavioral change remains harder than technological change. Organizations must rethink ownership, accountability, and trust before they can fully benefit from agent-based development.

• The path to AI-native development is iterative. Mercari expects continued setbacks and learning cycles, applying what they describe as the Stockdale Paradox: maintaining confidence in the destination while remaining honest about current challenges.

In this episode, we cover:

(00:00) Intro

(01:46) Mercari’s scale and engineering culture

(02:51) DX awards at Mercari

(03:44) Mercari’s push to become AI-native

(06:34) The mandate to rethink everything

(08:02) Mercari’s AI visibility problem and how they solved it

(11:30) Mercari’s early findings on AI implementation

(18:47) Closing the AI awareness gap at Mercari

(21:11) Mapping AI opportunities across Mercari

(31:32) Unpacking the results from the second rollout

(34:14) Agent spec-driven development and what’s next

(37:37) A multi-loop SDLC

(40:50) Some hard lessons

(42:55) Closing thoughts

Where to find Michael Galloway:

• LinkedIn: https://www.linkedin.com/in/michaelroygalloway

• X: https://x.com/michaelgalloway

Where to find Snehal Shinde:

• LinkedIn: https://www.linkedin.com/in/snehal-shinde

Referenced:

• Mercari

• Cursor

• Devin

• Claude Code | Anthropic’s agentic coding system

• GitHub

• Datadog

• Tim Bozarth - Microsoft | LinkedIn

• Airbnb

• Jim Collins - Concepts - The Stockdale Paradox

Kelly Anne Pipe is Head of Developer Experience at Vanguard, and Nicole Scribner is a Director in the firm’s Chief Technology Office focused on engineering enablement and advancement.

In this session from DX Annual, Kelly Anne and Nicole share how Vanguard is expanding its AI strategy beyond software engineering to the entire product development lifecycle. While the company initially focused on tools like GitHub Copilot for engineers, they found that faster coding alone did not significantly improve delivery speed. Product managers, designers, QA teams, and organizational processes were still operating at a different pace.

To address this challenge, Vanguard developed a product team maturity model built around three stages: Augmented, Accelerated, and Autonomized. The framework spans six dimensions, from AI-powered delivery and AI-ready codebases to team autonomy, operations, and responsible AI.

Kelly Anne and Nicole explain how Vanguard is applying the model across more than 800 product teams, the behaviors they believe will enable faster delivery, and the lessons they have learned about measurement, organizational change, dependencies, and scaling AI across the product development lifecycle.

Some takeaways:

Beyond the engineering bubble

• Faster coding does not automatically lead to faster delivery. Vanguard found that while engineers using AI tools reported significant productivity gains, product managers, designers, QA teams, and governance processes were still operating at traditional speeds.

• AI adoption becomes fragmented when it is treated as an engineering initiative. Organizations that focus solely on developer tooling risk creating an “engineering bubble” where one part of the product team accelerates while the rest of the workflow remains unchanged.

• The goal is to optimize the entire product development lifecycle. Vanguard shifted its focus from helping engineers code faster to helping cross-functional product teams move faster from idea to production.

The AI maturity model

• Vanguard built a maturity model around three stages: Augmented, Accelerated, and Autonomized. The framework gives more than 800 product teams a shared language for discussing AI adoption and long-term transformation.

• The model spans six dimensions of AI maturity. These include AI-powered product delivery, AI-ready codebases, agent-powered workflows, AI-augmented operations, team autonomy and enablement, and responsible AI.

• The goal is organizational transformation, not tool adoption. The framework focuses on how entire product teams evolve as AI becomes embedded throughout the product development lifecycle.

Building AI-ready foundations

• AI readiness starts with the fundamentals. Documentation, testing, CI/CD pipelines, architecture decisions, and code quality all become more important when agents are introduced into the development process.

• The codebase becomes the interface between teams and AI agents. Poor documentation, weak test coverage, and slow feedback loops limit the effectiveness of even the most capable AI tools.

• Dependencies become more visible at agent speed. Processes that were merely frustrating for humans become major bottlenecks when AI can complete implementation work in hours rather than days.

Scaling AI beyond engineering

• Every role on the product team needs AI-specific workflows. Vanguard is focused on helping product managers, designers, QA teams, and engineers incorporate AI into their daily work rather than limiting adoption to developers.

• The most valuable opportunities often begin before coding starts. AI can help transform customer conversations, discovery work, requirements, and design artifacts into implementation-ready inputs.

• Agent orchestration changes the role of the human. As agents take on more routine execution work, people increasingly act as orchestrators, reviewers, and strategic decision-makers.

The challenges of adoption and measurement

• Behavior change is harder than deploying tools. Vanguard found that fear, uncertainty, and questions about job security often create bigger barriers to adoption than technology itself.

• Simple productivity metrics can be misleading. Measures such as lines of code generated or time saved per developer do not capture whether AI is creating meaningful business value.

• Organizations need layered measurement strategies. Adoption metrics, process improvements, cycle time, quality, and customer outcomes all need to be considered together to understand AI’s true impact.

Lessons from the AI transition

• Agent speed exposes organizational debt. Slow approvals, review queues, onboarding processes, and governance workflows become much more obvious when implementation work accelerates.

• Responsible AI can accelerate delivery rather than slow it down. Investing in guardrails, governance, security, and automated controls early enables teams to move faster with greater confidence.

• The biggest opportunity is organizational transformation. Vanguard believes the future belongs to companies that redesign entire product teams around AI rather than simply adding AI tools to existing workflows.

In this episode, we cover:

(00:00) Intro

(02:16) The state of AI one year ago at Vanguard

(02:54) The engineering bubble

(05:05) Building an AI maturity model for 800 product teams

(08:24) Dimension 1: AI-powered product delivery

(10:00) Dimension 2: AI-ready codebase

(12:20) Dimension 3: Autonomous agent utilization

(13:00) Dimension 4: AI-augmented operations

(14:00) Dimension 5: Team autonomy and enablement

(16:11) Dimension 6: Responsible AI

(18:15) The people problem: role evolution

(20:00) The measurement problem

(22:55) Lessons learned from rolling out the maturity model

(26:46) What’s ahead

(30:10) Q&A #1: Getting your codebase ready for AI

(32:22) Q&A #2: Audit trails and responsible AI

(34:16) Q&A #3: Vanguard’s maturity model progress

(36:15) Q&A #4: Measuring cycle time across 800 teams

Where to find Nicole Scribner:

• LinkedIn: https://www.linkedin.com/in/nicole-scribner-35b80422a

Where to find Kelly Anne Pipe:

• LinkedIn: https://www.linkedin.com/in/kellyannepipe

Referenced:

• Vanguard

• Jennifer St Pierre - Dell Technologies | LinkedIn

• Mercari

In this session from DX Annual, Eleanor Millman, Senior Staff Product Manager, and Mina Tawadrous, Associate Director of Product Management at SiriusXM, share how their platform engineering organization developed a prioritization framework for platform engineering teams serving hundreds of developers across a complex cloud platform.

They explain how they define and weight platform-specific impact factors, use developer data to refine priorities, and score projects more consistently. They also explore why prioritization debates often stem from conflicting, invisible, or outdated assumptions, and how SiriusXM began treating assumptions like code by documenting, versioning, and reviewing them in source control.

Finally, they demonstrate how AI can surface assumptions, connect initiatives to existing knowledge, and support project scoring while keeping humans in the loop. Throughout the session, they offer a practical framework for making prioritization decisions more transparent, data-driven, and scalable.

Some takeaways:

Building a platform engineering prioritization framework

• Platform engineering requires different prioritization criteria. SiriusXM found that traditional product metrics did not fully capture the value of platform engineering work, leading the team to define platform-specific impact factors around development speed, reliability, security, cost, platform efficiency, user trust, and data-driven decision making.

• A simple scoring model created a shared language for prioritization. The framework combined impact, urgency, effort, and business needs to help teams compare projects consistently and explain why certain initiatives were prioritized over others.

• The framework evolved alongside the organization. As company priorities changed after a major platform launch, SiriusXM adjusted impact factor weights to reflect new goals around cost optimization, technical debt reduction, and data maturity.

Using developer data to guide decisions

• Developer feedback helped shape prioritization. Rather than relying solely on intuition, the team used survey data and other developer insights to determine where additional investment would have the greatest impact.

• Impact factor weights were revisited regularly. Quarterly reviews allowed the team to adjust priorities based on changing business objectives and improvements in areas such as reliability and security.

• Data increased confidence in prioritization decisions. By grounding discussions in evidence, teams were able to align more effectively on where to invest their limited capacity.

Treating assumptions like code

• Many prioritization conflicts stem from assumptions rather than priorities. Teams often disagreed because they were working from different, invisible, or outdated assumptions about users, workflows, and business needs.

• Documenting assumptions improved organizational alignment. SiriusXM began storing assumptions in source control, making them easier to discover, review, update, and validate over time.

• Debates became more productive when assumptions were explicit. Instead of arguing over which project mattered most, teams focused on validating the underlying beliefs that informed their decisions.

Using AI to surface organizational knowledge

• Assumption repositories became difficult to navigate at scale. As more assumptions were documented, it became increasingly difficult for individuals to find relevant context and connections across projects.

• AI helped uncover relationships humans might miss. By searching assumption repositories, OKRs, and prior project data, AI was able to surface relevant information that would otherwise be difficult to discover.

• AI improved information recall rather than replacing judgment. The goal was not automated decision making but helping teams access the knowledge needed to make better decisions.

Building an AI-assisted prioritization workflow

• AI can guide teams through the scoring process. SiriusXM built workflows that ask clarifying questions, surface assumptions, identify relevant organizational context, and generate initial project scores.

• Human validation remains essential. Teams review assumptions, challenge recommendations, and approve updates before information is added back into the system.

• Each prioritization cycle strengthens the knowledge base. New assumptions, decisions, and project context become available for future initiatives, making the system more valuable over time.

Keeping humans in the loop

• The framework is designed to support conversations, not replace them. Scores help teams discuss priorities more objectively, but important decisions still require context and judgment.

• Stakeholder disagreements often reveal useful information. When the framework produces results that feel wrong, the discussion can uncover missing assumptions, incomplete data, or opportunities to improve the model itself.

• The framework continues to evolve. SiriusXM treats both the prioritization model and the supporting AI tools as products that require ongoing iteration, feedback, and refinement.

In this episode, we cover:

(00:00) Intro

(02:58) Building a platform engineering prioritization framework

(04:59) The seven platform engineering impact factors

(09:38) Using impact factors to score projects

(13:11) Using developer data to refine priorities

(16:33) Three ways assumptions fail

(17:40) Assumptions as code

(21:00) New problems created by assumptions as code

(22:00) Using AI to surface assumptions

(23:44) Building an AI-powered feedback loop

(25:44) Inside the AI prioritization tool

(28:18) Three steps to build your own framework

(30:02) Q&A #1: Evaluating high-cost projects

(31:30) Q&A #2: The cadence of iteration

(32:10) Q&A #3: When the framework conflicts with a stakeholder’s priorities

(35:26) Q&A #4: Using the framework for non-developers

Where to find Eleanor Millman:

• LinkedIn: https://www.linkedin.com/in/eleanor-millman-98b10350

Where to find Mina Tawadrous:

• LinkedIn: https://www.linkedin.com/in/mina-tawadrous

Referenced:

• AWS

• Databricks

• RICE: Simple prioritization for product managers

• Designing developer experience surveys

• GSB Preserve | View | The Curse of Knowledge

Brian Scanlan is a Senior Principal Systems Engineer at Intercom, where he works on platform engineering, developer productivity, and AI adoption across the company.

In this session from DX Annual, Brian shares how Intercom set out to double engineering throughput and ultimately achieved that goal in nine months. Rather than treating AI as an optional productivity tool, the company standardized on Claude Code, updated performance expectations, invested heavily in enablement, and adopted an agent-first approach to technical work.

Brian explains why Intercom views Claude Code as a platform rather than a tool, how the company is building domain-specific skills and workflows for agents, and why it believes agents should eventually be able to perform any technical task a senior engineer can complete on a laptop.

He also shares the data behind Intercom’s AI adoption efforts, including gains in throughput, reductions in defect backlogs, improvements in code quality, and the growing use of automated pull request approvals. Throughout the talk, Brian offers a practical look at what it takes to scale AI adoption across a large engineering organization and the lessons Intercom has learned along the way.

Some takeaways:

Doubling engineering throughput

• Intercom set a goal to double engineering throughput. Rather than focusing on AI adoption metrics, the company chose a concrete business outcome: doubling merged pull requests per member of the R&D organization.

• The goal was achieved in nine months. Intercom ultimately doubled PR throughput and later tripled it over a 16-month period, with no signs of the trend slowing down.

• Throughput was treated as a reasonable proxy for impact. While Brian acknowledged that every metric has flaws, he argued that organizations adopting AI at scale should expect to see meaningful increases in output.

Managing organizational change

• AI adoption became part of the job. Intercom updated expectations for engineers, designers, and product managers so that using AI tools effectively became part of performance expectations rather than an optional activity.

• The company combined incentives with support. Hackathons, enablement days, dedicated support teams, leadership messaging, and public recognition all helped accelerate adoption.

• Leadership stayed relentlessly on message. Repeating the same goals and expectations across every forum helped create clarity about the direction of the organization.

Why Intercom standardized on Claude Code

• Intercom chose a single AI platform. Rather than allowing teams to fragment across different tools and workflows, the company standardized on Claude Code and invested heavily in making it work well.

• The real value comes from context, not models. Brian argued that domain knowledge, skills, documentation, workflows, and organizational context create far more value than constantly switching between models.

• Agents should be treated like new employees. Intercom’s goal is to onboard agents the same way it would onboard a senior engineer by giving them access, training, tools, documentation, and clear expectations.

Building an agent-first engineering organization

• All technical work is becoming agent-first. Intercom believes that any task a human can perform on a laptop should eventually be accessible to agents.

• The focus is on durable capabilities rather than custom AI infrastructure. Teams are encouraged to build skills, access patterns, and workflows that will remain valuable even as models and tools continue to evolve.

• Agents should solve problems, not just execute commands. Rather than telling agents exactly which skill to run, engineers increasingly describe the problem and allow agents to determine the best workflow.

Skills as organizational knowledge

• Intercom has built hundreds of reusable skills. These skills capture domain expertise, troubleshooting processes, coding standards, operational procedures, and other institutional knowledge.

• High-quality skills create leverage across the organization. Once a skill exists, every engineer can benefit from the expertise embedded within it, even if they were not involved in creating it.

• Skills continuously improve over time. Engineers are encouraged to update skills whenever new knowledge is discovered so that lessons learned become available to everyone.

Measuring the impact of AI adoption

• Nearly all pull requests are now authored by Claude. Brian shared that more than 95% of pull requests are created with AI assistance, while automated pull request approvals continue to grow.

• Saved time is being reinvested into quality. As teams gained efficiency, they spent more time reducing technical debt and fixing defects, leading to a significant reduction in Intercom’s defect backlog.

• Code quality improved alongside throughput. Research conducted with Stanford showed that recent code changes were improving the overall quality of the codebase rather than degrading it.

The future of agentic software development

• Intercom wants agents to participate throughout the software development lifecycle. The company is replacing runbooks, expanding automation, and building remote agent capabilities that move work beyond individual laptops.

• AI adoption has expanded far beyond engineering. More than a thousand employees use Claude Code weekly, including teams in finance, operations, and other business functions.

• The biggest changes may still be ahead. Brian believes AI will reshape planning, team structures, workflows, and engineering roles over the coming years, not just how code is written.

In this episode, we cover:

(00:00) Intro

(02:54) Intercom’s goal of doubling throughput

(07:30) The platform strategy

(09:30) Their agent-first strategy

(10:58) Evergreen capabilities vs custom tooling

(12:28) How Intercom works with agents

(16:43) What the data reveals about AI adoption and impact

(19:20) Using session data to improve AI workflows

(20:20) Cutting the defect backlog in half

(22:44) Inside Intercom’s Claude Code setup

(28:09) Claude Code beyond engineering

(30:49) Q&A #1: Token cost

(32:52) Q&A #2: Preparing for AI pricing changes

(34:14) Q&A #3: Stress testing and auditing skills

(36:31) Q&A #4: Criteria for agents approving PRs

Where to find Brian Scanlan:

• LinkedIn: https://www.linkedin.com/in/scanlanb

• X: https://x.com/brian_scanlan

• Website: https://brian.scanlan.ie

Referenced:

• Intercom

• Software? No Way. We’re an A.I. Company Now! - The New York Times

• Anthropic

• Snowflake

• Linear

• LaunchDarkly

• Fin AI

• Microsoft Copilot

• Cursor

• Claude Code | Anthropic’s agentic coding system

• Steve Yegge (@Steve_Yegge) / Posts / X

• Honeycomb

• Fin Ideas

• Fin CLI | AI Agent Command Line Interface

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🗓 Join Justin Reock and me on June 18th for a research briefing on measuring AI agents, revisiting the Core 4, and more. Register here.

Last month, I had the privilege of attending the inaugural Developer Experience Research Forum at UC Irvine. It brought together researchers and practitioners from across academia and industry for a day of talks, conversations, and the kind of honest debate that only happens when the right people are in the same room.

The day included a panel that I won’t ever forget. For the first time since the SPACE framework was published in February 2021, all six of its authors were together in person: Nicole Forsgren, Margaret-Anne (Peggy) Storey, Chandra Maddila, Thomas Zimmermann, Jenna Butler, and me. I want to start by saying thank you to each of them. Collaborating on SPACE has been one of the most meaningful experiences of my career, and getting to sit alongside these colleagues five years later and take stock of what the framework has become was genuinely moving. I’m grateful to Tom, Iftekhar Ahmed, and the UCI team for making it happen, and to André van der Hoek for his amazing job moderating.

Unfortunately I don’t have a recording to make available to those who were unable to attend, but here is my attempt to capture the highlights of that conversation. The panel was wide-ranging, driven largely by questions from the audience, and covered a lot of ground. I’ll do my best to do it justice.

How SPACE came to be

For those less familiar with the backstory, SPACE did not emerge from a formal research program or a planned initiative. The idea started with Nicole. DORA, which she co-created, provided a measurement framework for software delivery, but there was a need for something that addressed developer productivity more broadly. So she reached out to a handful of colleagues (which thankfully included me), arrived with a few dimensions sketched out in her head, and the rest took shape over a series of Teams calls during what was still largely a remote-work world.

What’s remarkable is that several of us had never met in person before that project. We built the framework together, at a distance, and then watched it travel far beyond anything we had imagined. As I said on the panel:

“We could have never imagined that it was going to sort of grow into the thing it became. I don’t think you should ever hope to do something like that, because you’ll never be able to quite capture it. It’s like... right place, right time, things came together.”

The framework itself is straightforward in concept: five dimensions to consider when thinking about developer productivity. Satisfaction and wellbeing. Performance. Activity. Communication and collaboration. Efficiency and flow. The core argument is that productivity cannot be reduced to a single metric, and that a meaningful measurement approach should draw from at least three dimensions and include at least one perceptual measure. Metrics chosen well will often create productive tension with each other, and that tension is a feature, not a flaw.

Chandra reflected on the process of building it, including a detail I had honestly forgotten: the framework's original working name was not SPACE at all. It was FACTS. Trust was in there from the beginning, under a different label. Looking back on the framework more than five years later, Peggy said:

“I think we did a really good job. I think that the five dimensions have really held up really well. But they’re big — each of those dimensions are such huge concepts. And maybe what we need to do now is look at each of these in turn.”

Activity metrics: newly controversial, newly important

No dimension generated more discussion on the panel than Activity, and that’s not an accident. Activity metrics are the ones most organizations default to because they’re the easiest to instrument. They’re also the ones most prone to misuse.

What made the conversation interesting is that the panel did not argue for abandoning Activity measurement. The argument was more nuanced than that. Jenna put it directly:

"I actually think this is one of the areas where SPACE is newly important again, because you may have seen headlines about what percent of codebases are AI generated at this point. And I'm like, we're back there. We wrote about this a decade ago... some of those activity metrics like lines of code and PRs are newly resurfacing, and people are forgetting that we knew that this wasn't the greatest plan in isolation."

Chandra added that the scale has changed in a way that makes the problem even more acute. A single developer working with a swarm of agents can now generate an extraordinary volume of pull requests. The count alone tells you almost nothing about the quality, the impact, or the experience of the work.

The more useful question is not whether to measure activity, but which activity metrics are worth measuring and what you plan to do with them. I offered an example from my own work: Time-To-First-PR, meaning how long it takes a new hire to check in their first piece of code.

“Obviously easy to game, right? Have a new hire check in a trivial first PR... Turns out when you try to game it, when you explicitly try to have a trivial first check-in, it still leads to positive long-term outcomes. Why? Because that first code check-in has nothing to do with the code. It’s about learning your environment, setting up your system.”

I believe that good metric design involves choosing metrics where gaming them still gets you the outcome you actually want.

The politics of productivity measurement

One of the most candid moments of the panel came in response to an audience question about whether productivity measurement is inherently neutral or whether it inevitably becomes a political tool. The honest answer is that it is both, and you have to design for that reality.

Tom made the point that having five dimensions rather than one makes it structurally harder to play politics with the data. When you look at multiple dimensions simultaneously, you’ll often find they point in different directions, and that tension forces more careful thinking.

Jenna was direct about something that deserves to be said plainly. There is an elephant in the room across the industry right now about how many developers organizations need, and productivity metrics are being watched closely in that context.

"We tend to decouple from products and we're very... hoard-y with our data. We will give them trends. We'll let them know this is what's happening on a broad scale, or doing this had this impact. But we are not allowing individual managers, directors to look at people's information. We protect that because in theory, happy workers are productive workers. People who are terrified are not."

… and Nicole added additional framing that I found useful:

"Some data is better than no data... I know that for many of us here, we really do our best to measure in a way that is very neutral. But I know I'll have execs and other business divisions come to me and they'll say, 'Well, I need this [metric] to go up.' And I was like, 'Amazing. That's not on me. That's on you. I can give you the information and you can figure out if it goes up [or] down and why.'"

One practical safeguard worth noting is that some organizations deliberately bucket metrics together, so that no one can drive up a single number without being held accountable for the others in the cluster. It makes the kind of narrow gaming that distorts incentives structurally harder to do.

The C in SPACE: the most underinvested dimension

If Activity is the dimension that gets the most attention, Communication and Collaboration may be the one that gets the least. That gap is growing more consequential, and as Peggy put it, needs more focus than ever:

“Development is a team sport. And with AI, I don’t think there’s anyone in this room that doesn’t think that collaboration and communication hasn’t changed... If anybody here is thinking of using SPACE, make C one of the first things you look at.”

What makes this particularly important right now is that AI has changed collaboration patterns in ways we are only beginning to understand. Developers report asking questions of their AI tools that they used to ask colleagues. The texture of team communication is shifting. And yet the measurement infrastructure for tracking that dimension barely exists in most organizations. I shared a finding from my SPACE of AI paper that felt particularly relevant:

“C is the only dimension of SPACE that the majority of developers do not believe that AI has improved. Every other dimension showed improvement. Not C.”

That's a significant signal, and I think it points toward where the field needs to invest its energy next.

What would you add to SPACE today?

The question the audience asked that will stay with me longest was a simple one: if you were writing SPACE now, what would you add?

The five dimensions have held up well. The panel was in agreement on that. But five years of AI acceleration, remote and hybrid work, and a rapidly shifting sense of what software development even means has surfaced things the original framework didn’t fully anticipate. While we might not add new dimensions, if we were updating it today, we would add focus for:

• Trust: This was the most consistent answer across the panel, acting as a foundational bedrock for satisfaction and performance.

• Cognitive and Intent Debt: Based on Peggy’s recent work, are we losing overall understanding of codebases as AI writes more of them?

• Deskilling: The worry that relying heavily on automated tools will cause core engineering capabilities to atrophy over time.

• AI Addiction: Within the wellbeing dimension, tracking addiction-like behaviors with generative AI tools.

Ultimately, as Chandra mentioned, the strength of SPACE is that organizations can dial up or down different dimensions to meet rising needs. You don’t need to change the structure; you just need to rebalance the rubric.

"Things like well-being are very, very, very important. So I think reducing focus a little bit on the activity side... that doesn't fundamentally change what SPACE is. You can just use SPACE but rebalance the rubric."

We acknowledge that the framework was never designed to be a perfect model. But, as Peggy put it, that doesn’t mean it isn’t valuable.

"Some models are wrong, some are useful. It was supposed to change the conversation. It was supposed to make people think about the different aspects of productivity. And I think it did that."

That feels right to me. The framework was designed to change the conversation. I think it did. The work now is to keep refining what we measure within that space, with the same care we brought to defining it in the first place.

Final thoughts

It was a remarkable day for me. I’m grateful to UC Irvine for hosting it, to my co-authors for showing up, and to everyone in that room for asking the hard questions. If the industry continues to ask them with this much rigor and honesty, I think the next five years will be even more interesting than the last.

This week’s featured DevProd job openings. See more open roles here.

• Ashby is hiring an Staff Platform Engineer | Remote

• BambooHR is hiring a VP of Engineering | Utah (Hybrid)

• Cashea is hiring an Infrastructure & Developer Productivity Platform Engineering Manager | Remote

• Figma is hiring a Staff Software Engineer, Developer Experience | Remote; US

• Morgan Stanely is hiring an AI Platform Engineer - Vice President | New York

That’s it for this week. Thanks for reading.

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Abi Noda is joined live at DX Annual by three engineering leaders shaping AI adoption at scale: Tim Bozarth, Corporate Vice President in Microsoft’s CoreAI division; Nancy Wang, CTO of 1Password; and Taroon Mandhana, CTO of AI and Teamwork at Atlassian. Together, they discuss how AI is changing engineering organizations, from team structures and planning cycles to hiring, governance, and measurement.

The panel explores how the profile of a great engineer is evolving, why smaller cross-functional teams are becoming more effective, and what happens when product managers, designers, and customer support teams start contributing code. They also share why they are encouraging AI adoption through enablement, training, and local champions rather than mandates, and how AI is shifting more of the software development lifecycle toward planning and validation.

Finally, they discuss where human judgment remains essential, how to measure adoption and manage token usage, and how to connect AI investments to business outcomes while preserving room for experimentation and learning.

Some takeaways:

Rethink team structures for faster learning

• Smaller teams are becoming more effective for zero-to-one work. AI reduces the cost of implementation, making alignment and rapid iteration the primary bottlenecks when searching for product-market fit.

• Planning cycles are getting shorter. Instead of locking in 12- to 18-month roadmaps, teams are shifting toward quarterly planning to adapt more quickly to changing technology and market conditions.

• Large-scale org changes can wait. Several panelists emphasized that experimentation and learning should come before major structural redesigns.

The best engineers think like makers

• A maker’s mindset matters more than mastery of a specific tool. The most effective engineers stay focused on outcomes and use whatever tools help them build valuable products.

• Product and engineering skills are converging. Strong engineers increasingly combine technical depth with product judgment, customer empathy, and design sensibility.

• Agency is becoming a defining trait. Engineers who can work across functions, navigate ambiguity, and drive decisions are gaining leverage as AI handles more of the implementation work.

Expect more people to participate in software creation

• Prototypes are replacing long-form requirements documents. Interactive demos often lead to faster and more productive conversations than detailed specifications.

• Product managers, designers, and customer support teams are starting to write code. AI is lowering the barrier for non-engineers to contribute directly to the software development lifecycle.

• Quality systems become more important as more people contribute code. Robust tests, review processes, and deployment safeguards are essential when more people can generate production code.

Drive adoption through enablement, not mandates

• Outcomes matter more than activity. The goal is not maximizing AI usage for its own sake, but improving speed, ease, and product quality.

• Local champions accelerate adoption. Teams learn fastest when respected peers demonstrate practical ways to use AI in real workflows.

• Activity metrics are diagnostic, not the objective. Low usage can signal where teams need more training, support, or better tools.

The AI-native SDLC shifts work toward planning and validation

• Plan and validate are becoming the highest-leverage activities. As AI accelerates code generation, more human effort shifts toward defining what to build and evaluating whether it meets expectations.

• Operations and incident response are ripe for automation. Engineering teams are beginning to use AI to triage alerts, investigate incidents, write postmortems, and reduce the time spent on routine operational work.

• Human judgment remains essential. Leaders were unanimous that critical decisions around quality, security, and accountability still require people in the loop.

Measure outcomes, costs, and learning

• Token usage is becoming the new cloud bill. Engineering leaders are applying FinOps-style discipline to monitor and forecast AI spending.

• North Star metrics provide a clearer signal. Examples include idea-to-value, innovation time, and product quality.

• Experimentation deserves budget. Some AI usage will not generate immediate ROI, but it can create learning that compounds into long-term competitive advantage.

In this episode, we cover:

(00:00) Intro

(01:08) Introducing the panelists

(02:16) AI’s impact on engineering team structures and planning cycles

(05:00) How the role of the engineer is changing and what makes a great engineer

(10:11) The opportunities and challenges of non-engineers writing code

(15:26) Encouraging AI adoption without mandating it

(21:25) What an AI-native SDLC looks like and why human judgment still matters

(30:56) Measuring AI adoption, token usage, and ROI

(37:06) How to tie AI investments to business outcomes

Where to find Nancy Wang:

• LinkedIn: https://www.linkedin.com/in/wangnancy

Where to find Taroon Mandhana:

• LinkedIn: https://www.linkedin.com/in/taroonm

Where to find Tim Bozarth:

• LinkedIn: https://www.linkedin.com/in/tbozarth

Where to find Abi Noda:

• LinkedIn: https://www.linkedin.com/in/abinoda

Referenced:

• DX Core 4 Productivity Framework

• Microsoft

• 1Password

• Atlassian

• Jira

• Confluence

• Loom

• Rovo

• Amazon Operating Cadence - Working Backwards

Jennifer St Pierre is Senior Vice President of Developer Experience and Transformation at Dell Technologies, where she leads the strategy for how Dell’s Infrastructure Solutions Group builds, operates, and evolves software.

In this session from DX Annual, Jen argues that the biggest challenge in adopting agentic AI is not the technology itself, but the people transition behind it. Drawing on lessons from earlier shifts like Agile, DevOps, and cloud adoption, she explains why organizations that treat AI as a simple tooling rollout may get compliance, but not commitment.

Jen outlines five leadership imperatives for navigating the transition: building a shared understanding of why change is happening, defining a clear future state, clarifying how roles will evolve, creating psychological safety for experimentation, and aligning metrics and organizational structures with new ways of working. Throughout the talk, she emphasizes that while AI may generate code, humans remain responsible for direction, judgment, and meaning.

Some takeaways:

Treat AI adoption as a people transformation

• Technology transitions are really people transitions. The hardest part of adopting agentic AI is not the tooling itself, but helping engineers understand how their roles, workflows, and career paths will evolve.

• Compliance is not the same as commitment. Organizations that treat AI as a tooling rollout may achieve adoption metrics, but they will struggle to build the trust and engagement needed for lasting change.

• Every major platform shift follows a familiar pattern. New technologies create excitement, skepticism, fear, and eventually productivity gains that become the new normal.

Build a shared understanding of why AI adoption matters

• Start with an honest explanation of why the change is happening. If developers do not understand the business rationale, they are likely to assume the initiative is primarily about cost reduction.

• Shared understanding does not require universal agreement. It means everyone is working from the same candid view of market pressures, strategic goals, and organizational intent.

• Framing shapes emotional response. Positioning AI as a way to help engineers focus on more strategic work creates a very different reaction than simply saying it will increase productivity.

Define a clear future state

• A vague vision creates fear. When people cannot picture what their work will look like in 12 to 18 months, they tend to imagine replacement, stagnation, or obsolescence.

• Role clarity is essential. Teams need to understand what skills will matter, how performance will be measured, and which responsibilities will increase or diminish.

• Specificity beats slogans. Concrete expectations about how AI will be used help people see where they fit in the new model.

Create psychological safety for experimentation

• Teams need permission to make mistakes. AI adoption requires experimentation, and experimentation inevitably involves missteps and imperfect results.

• Psychological safety helps teams surface problems earlier. When engineers feel safe speaking up, leaders get better information and can address issues before they escalate.

• Silence is expensive. Leaders who discourage candor risk making decisions based on filtered or incomplete information.

Align metrics and organizational structures

• Old metrics can reinforce old behaviors. Measuring lines of code or heroic firefighting may encourage exactly the habits AI should help organizations move beyond.

• Metrics and structure must evolve together. Governance, incentives, funding, and performance systems need to support the behaviors leaders want to see.

• Transformation should survive without constant reminders. If the desired behaviors disappear as soon as leaders stop talking about them, the change has not yet become embedded.

Lead the transformation intentionally

• Leaders must model the change themselves. Using AI tools, sharing lessons learned, and being transparent about failures builds credibility.

• Career paths must be made explicit. Engineers want to know how they can continue to grow and whether deep technical expertise will remain valuable.

• AI may generate code, but humans generate direction. Judgment, context, and meaning remain the most valuable contributions people bring to software development.

In this episode, we cover:

(00:00) Intro

(00:13) Why every major technology shift is ultimately a people transition

(05:00) AI-generated code and the evolving role of software engineers

(07:43) The importance of developing a shared understanding

(12:00) Defining a clear future state and how engineering roles will evolve

(19:12) How psychological safety enables experimentation and honest feedback

(22:41) Why metrics and organizational structure must evolve for the age of AI

(25:40) Why leaders must drive AI transformation intentionally

Where to find Jennifer St Pierre:

• LinkedIn: https://www.linkedin.com/in/jennifer-st-pierre-4935a81

Referenced:

• Measuring developer productivity with the DX Core 4

• Understand team effectiveness

Jason Valentino is Head of Software Engineering Strategy at BNY, where he oversees developer tooling, DevEx, platform workflows, and software delivery governance across more than 8,000 engineers.

In this session from DX Annual, Jason shares how BNY moved beyond AI coding assistants to rethink the entire software delivery lifecycle. He explains how his team identified bottlenecks across the SDLC, prioritized automation opportunities, and applied AI to planning, peer review, testing, change management, and compliance workflows.

Jason also discusses what it takes to scale AI inside a highly regulated enterprise, including rewriting policies, partnering closely with risk and audit teams, and building a culture that encourages experimentation and rapid sharing of ideas.

Some takeaways:

Start with the 3X stress test

• Ask what breaks if engineering throughput triples. Jason’s team began by assuming AI would dramatically increase the volume of pull requests, code reviews, and releases, then identified which systems and processes would become bottlenecks.

• Map every step of the SDLC. BNY listed each task across planning, coding, testing, peer review, and release governance to understand which steps were manual, partially automated, or already well-instrumented.

• Use developer sentiment to prioritize investments. By combining workflow analysis with DX survey data, the team focused on the areas causing the most friction rather than chasing the latest AI use case.

Apply AI in three distinct ways

• Use IDE and CLI tools to amplify individual developers. Tools like Claude Code, Windsurf, and Codex help engineers move faster while still working within established guardrails.

• Deploy autonomous agents for repetitive work. BNY’s “digital workers” handle tasks like access requests, backlog grooming, and other low-value activities that engineers would rather avoid.

• Embed AI directly into workflows. The biggest gains come when AI is triggered automatically as part of code review, change management, and testing rather than relying on developers to invoke tools manually.

Use small automations to compound over time

• Automate the tedious parts of planning. BNY added AI capabilities to Jira to draft stories and epics, lint requirements, and assign confidence scores.

• Turn one automation into the next. Once a high-quality story exists, it becomes the foundation for generating test cases and other downstream artifacts.

• Look for highly manual actions. Jason recommends watching how teams actually work and identifying repetitive tasks that are prime candidates for automation.

Rebuild governance for an AI-assisted world

• Rewrite policies and controls. Existing language around code review, approvals, and software delivery often assumes humans perform every step and must be updated to reflect AI-assisted workflows.

• Bring risk and audit teams in early. Rather than presenting finished solutions for approval, BNY collaborates with governance partners while designing new approaches.

• Codify deterministic rules. AI can handle routine work automatically, while larger or riskier changes are routed to humans for additional oversight.

Treat duplication as a feature, not a bug

• Expect multiple teams to solve the same problem. In a large organization, some overlap is inevitable when thousands of people are experimenting with AI.

• Use show-and-tell to surface innovation. BNY hosts weekly sessions where teams demonstrate what they’ve built and share lessons learned.

• Consolidate the best ideas. Once similar solutions emerge, platform leaders can combine the strongest features into shared capabilities.

Create a culture that rewards experimentation

• Start saying yes. Jason’s advice to engineering leaders is to lower barriers and put promising ideas in front of users quickly.

• Treat internal tools like products. Successful experiments are documented, shared, and iterated on rather than left as one-off hacks.

• Make engineering fun again. For Jason, one of the biggest wins of the past year has been seeing teams energized by the opportunity to solve meaningful problems with AI.

In this episode, we cover:

(00:00) Intro

(01:20) Early results from AI coding tools at BNY

(04:08) The 3X stress test: What breaks if engineering throughput triples?

(06:56) Three ways to apply AI across the SDLC: IDE and CLI tools

(08:07) Using autonomous AI agents for repetitive engineering tasks

(09:16) Embedding AI directly into SDLC workflows

(12:27) Why leaders should encourage experimentation and “start saying yes”

(15:00) Q&A: How platform and productivity teams are evolving to support AI

(16:33) Q&A: Rewriting policies and controls for AI-assisted software delivery

(17:52) Q&A: How AI is affecting software quality and test ownership

(19:00) Q&A: What Jason is most proud of: Practical examples of AI across the SDLC

(20:30) Q&A: How BNY handles duplicated work across AI initiatives

(22:30) Q&A: How BNY uses AI to support regulatory and compliance work

(23:30) Q&A: Automating code reviews and change tickets

(25:55) Q&A: How increased AI-driven throughput is affecting on-call and reliability

(27:11) Q&A: How BNY works with risk and audit partners to move quickly with AI

(29:01) Q&A: How BNY scales successful AI use cases across the organization

(30:42) Q&A: What Jason is most proud of after BNY’s busiest year with AI

Where to find Jason Valentino:

• LinkedIn: https://www.linkedin.com/in/jasonvalentino

Referenced:

• AI-assisted engineering: Q4 impact report

• Measuring AI code assistants and agents

• Measuring developer productivity with the DX Core 4

• Windsurf

• Claude Code by Anthropic | AI Coding Agent, Terminal, IDE

• Codex | AI Coding Agent

Recorded live at DX Annual, Abi Noda, co-founder and CEO of DX, joins Brian Houck of Microsoft to share an early look at DX’s new research on AI’s impact on engineering velocity.

Drawing on data from a sample of DX customers, they discuss what companies are actually seeing as AI adoption matures. Most organizations in the study saw pull request throughput increase by 10 to 15 percent—far more modest than the 10x gains often promised in industry headlines.

They explore why coding remains only a small part of developer work, where time saved by AI may be going, and the unintended consequences of moving faster, from shifting bottlenecks to “false velocity.” Abi also shares how engineering leaders are applying AI beyond coding and how DX is evolving its measurement framework to account for both human and agent productivity.

Some takeaways:

Most organizations are seeing modest gains from AI

• PR throughput is increasing by about 10 to 15 percent. Across DX’s sample, most organizations saw measurable improvements, but the gains were far smaller than the 10x productivity increases often cited in industry headlines.

• The median improvement was closer to 8 percent. While some organizations saw larger gains, the typical impact was more incremental than transformational.

• Even modest gains can be meaningful at scale. A 10 percent increase in throughput can represent a significant improvement when applied across hundreds or thousands of engineers.

Coding is only one part of the productivity equation

• Developers spend only about 14 percent of their time writing code. If AI primarily accelerates coding, its impact on overall engineering velocity will naturally be constrained.

• The biggest bottlenecks often lie elsewhere. Planning, reviews, testing, documentation, and coordination still consume the majority of engineering time.

• Time savings do not map neatly to output gains. Organizations can see meaningful reductions in coding effort without a proportional increase in pull request volume.

Why productivity gains are lower than many leaders expected

• Coding is not the primary bottleneck. Improving a small slice of the development process only moves the overall system so far.

• Automation creates new bottlenecks. Faster code generation can increase pressure on reviews, QA, and technical oversight.

• Social friction slows adoption. Skepticism, inconsistent usage, and unrealistic expectations can limit the benefits of AI tools.

• Tool and skill gaps compound over time. Engineers need both the right tools and the knowledge to use them effectively.

• AI tools still lack context. Limited understanding of business logic and codebase nuances can reduce output quality.

Beware of false velocity

• More code does not necessarily mean more business value. Teams can increase pull request counts without meaningfully accelerating roadmap delivery.

• Quality and cost remain critical concerns. Organizations are closely monitoring technical debt, token spend, and long-term maintainability.

• Faster output can create delayed consequences. The full impact of AI-generated code may not become apparent until months later.

The biggest opportunities lie beyond coding

• The remaining 86 percent of engineering work is the next frontier. Leaders are applying AI to planning, documentation, incident response, and other parts of the SDLC.

• Autonomous agents can augment human capacity. Instead of simply speeding up developers, organizations are exploring how agents can work in parallel.

• Developer experience still matters. Improving focus time, documentation, and workflow friction can amplify the benefits of AI.

Measurement frameworks are evolving

• Some metrics remain constant. Velocity, quality, and developer experience are still essential signals.

• Acceleration and augmentation should be measured separately. Leaders need to distinguish between human productivity gains and work performed autonomously by agents.

• Agent experience is an emerging concept. DX is beginning to survey AI agents directly to understand their constraints, bottlenecks, and effectiveness.

Cognitive debt is a new concern

• AI can reduce understanding while increasing output. Developers may ship code more quickly while building a weaker mental model of the systems they maintain.

• Short-term efficiency can create long-term costs. Reduced comprehension may make future debugging and maintenance more difficult.

• The long-term effects are still uncertain. Engineering leaders are only beginning to understand the human consequences of AI-assisted development.

In this episode, we cover:

(00:00) Intro

(00:53) What motivated DX’s research into AI’s impact on engineering velocity

(02:36) How DX designed the study and selected companies

(04:54) What DX’s data reveals about AI’s impact on engineering throughput

(06:31) Why PR throughput was the most practical metric to publish

(08:21) Why AI productivity gains are lower than many leaders expected

(10:24) How an all-in culture can amplify AI productivity gains

(12:35) Why it’s hard to track where AI-generated time savings are going

(15:04) Unintended consequences of AI-driven productivity gains

(17:12) Why leaders should look beyond coding to the rest of the SDLC

(19:43) Cognitive debt and the human costs of AI-assisted development

(21:33) How DX’s AI measurement framework is evolving

(24:42) How to make agents more effective

Where to find Brian Houck:

• LinkedIn: https://www.linkedin.com/in/brianhouck/

Where to find Abi Noda:

• LinkedIn: https://www.linkedin.com/in/abinoda

Referenced:

• DX Core 4 Productivity Framework

• DORA, SPACE, and DevEx: Which framework should you use?

• Time Warp: The Gap Between Developers’ Ideal vs Actual Workweeks in an AI-Driven Era - Microsoft • Research

• How Generative and Agentic AI Shift Concern from Technical Debt to Cognitive Debt

• Measuring AI code assistants and agents

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This week I’m sharing a new paper I co-authored with Jenna Butler, Margaret-Anne Storey, Travis Lowdermilk, Steven Clarke, and Emerson Murphy-Hill: 8 Myths on Software Engineering and GenAI. Drawing on recent large-scale studies, developer interviews, and field observations, the paper unpacks eight of the most persistent misconceptions about AI in software engineering.

Engineering leaders and teams can use this paper to set realistic internal expectations and benchmark their own AI ROI against real-world data.

My summary of the paper

The eight myths fall into three groups: how developers actually spend their time, how to measure AI’s impact, and how AI gets adopted in real organizations. The through-line is that the gap between AI’s promise and its measured impact has less to do with the models and more to do with the surrounding system of work.

What I hope makes this paper worth reading isn’t that any single myth is new; most have been circulating in the developer productivity research community for the last couple of years. What’s new is putting them side by side and showing how they reinforce each other.

Time, bottlenecks, and lines of code (Myths 1–3)

A 2025 study that I co-authored found that developers spend only 14% of their time writing code, consistent with prior research showing coding hovers between 11% and 18% of a typical day. The rest is design, meetings, review, coordination and administrative tasks.

That makes AI-as-code-generator a smaller lever than the headlines suggest, which aligns with DX’s findings that the typical organization is seeing a 7.8% increase in code throughput. Accelerating one phase can shift load into the next, and 8 Myths found that for one internal AI coding agent, only about half of the PRs were ultimately accepted, with 15% abandoned and 15% stuck waiting on a human reviewer.

The paper posits that measuring impact by lines of code, including AI-generated lines, repeats a mistake the field has known about for a decade. As Bill Gates put it: “Measuring software productivity by lines of code is like measuring progress on an airplane by how much it weighs.” Volume metrics incentivize the wrong behaviors and inflate the very review and quality burden that’s already the bottleneck.

To bridge the gap between bad metrics and true impact, many teams look to PR Throughput as a more complete measure of “work”. Tracking completed PRs is a significant step up from lines-of-code, and is a metric I have long supported, when used appropriately. However, even throughput requires caution in an AI era, and is important to use in a basket of metrics, as it is in the DX AI Measurement Framework.

Effects vary more than the headlines suggest (Myths 4–5)

The research on AI development tools is genuinely mixed. Some studies show large gains, others show neutral effects, and one study of experienced open-source developers found AI tools even increased implementation time by 18% on average.

The variance isn’t random. Familiar tasks benefit more than unfamiliar ones. Developer experience, motivation, and problem-solving style all shape outcomes. Even the prompt matters: one study found semantically equivalent rewrites produced different code in 46% of cases and changed correctness in 28%.

This is further proof why the “10x developer” narrative doesn’t hold up. Productivity gains measured on isolated, toy-sized tasks rarely survive contact with real codebases and real teammates, and prior research suggests much of the variance between developers is a property of the task, not the person.

Adoption is a systems problem, not an individual one (Myths 6–8)

Despite the headlines, only 10% of developers in one Microsoft survey expressed concern that AI tools might take their jobs. Most describe AI as expanding their creative capacity. More time on architecture, mentorship, and brainstorming, less on lookups.

But adoption itself is harder than the market assumes. The paper showed that 80% of developers use AI tools, but only 29% trust their accuracy. Recent research also identifies a “competence penalty”—developers, particularly women and older engineers, receive harsher evaluations for AI-assisted work even when the output is identical.

And almost all of the existing research still studies a single developer paired with a single tool, placing the burden of productivity on the individual. Historically, real productivity gains haven’t come from individuals optimizing their own work, they’ve come from systematic changes at the organizational level. AI may be the first technology where organizations have spent millions on licenses without a clear plan for how to extract value from them.

Final thoughts

The through-line across these eight myths is that AI’s impact in software engineering is shaped more by the system around the developer than by the developer themselves. Coding is a small share of the work. Lines of code is a poor measure. Effects vary by task, person, and context. And adoption depends on trust and organizational support far more than on tool capability.

For engineering leaders, that points to a different set of questions than the ones the market tends to ask. Not “how much code did AI write?” but “where in our system is AI actually relieving friction, and where is it just shifting pressure downstream?” Not “how do we get developers to use it?” but “what would it take for our developers to trust it?”

Treating AI adoption as an engineering systems problem, not a productivity hack, is what separates the organizations seeing real value from those still chasing the myths.

This week’s featured DevProd job openings. See more open roles here.

• Ashby is hiring an Staff Platform Engineer | Remote

• BambooHR is hiring a VP of Engineering | Utah (Hybrid)

• Cashea is hiring an Infrastructure & Developer Productivity Platform Engineering Manager | Remote

• Figma is hiring a Staff Software Engineer, Developer Experience | Remote; US

• Morgan Stanely is hiring an AI Platform Engineer - Vice President | New York

That’s it for this week. Thanks for reading.

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In “The SPACE Of AI”, we found that AI efficiency is a skill: as developers spend more time with these tools, their ability to extract value compounds. This is supported by another recent study which found that developers with higher usage were more likely to report AI making them more productive.

To explore this further, we looked at the trajectory of time savings from AI coding assistants: how quickly developers reach their peak gains, and whether those gains hold over time.

To explore this, we looked at a sample of DX data from over 500 companies over the last year (May 2025-April 2026). Our analysis focused on self-reported time savings, where developers estimated the number of hours per week they saved through the use of AI coding assistants. By tracking individual migration patterns between time-savings bands (categorized as low: <4 hrs/week and high: 6+ hrs/week), we could see how quickly gains are achieved and whether they are sustained.

What we’re seeing: Peak times savings are temporary

When we followed individual developers over four quarters to see how their time savings evolved, a few things stood out.

• Low initial savings often lead to higher future gains. Nearly one in three developers (31.4%) who started in the lowest time-savings band climbed to the highest band during the study period, indicating that early AI gains are a starting point rather than a fixed ceiling. In “The AI Native Developer,” we found that AI fluency increases with use, which raises an open question: how does this ramp-up in time savings map to the stages of AI fluency we identified?

• Time savings from AI ramp up quickly with continued use. Among developers who reached the highest time-savings band, roughly 7 in 10 (69.7%) got there in less than two quarters. This rapid progression is likely driven by a combination of factors, ranging from how quickly a developer adapts their workflow to how well their specific tasks align with AI capabilities.

• Peak time-savings are temporary, and may fade. Of those developers who reached peak time savings, two-thirds (66.1%) reported lower time savings in the quarters that followed.

  • Gains achieved in one quarter are not sustained. Among developers who hit the highest time-savings band in one quarter or less, just over half (50.5%) did not report that level of savings in future quarters. While we don’t know the exact cause of these fluctuations, the speed of the initial spike may reflect a burst of enthusiasm or a concentration of easy-win tasks but it does not translate into a durable shift in productivity.

  • A longer ramp-up does not show staying power. For developers who took two quarters to reach peak savings, the drop-off is even steeper. 79% did not report high time savings in future quarters. This suggests that even a more gradual adoption path does not insulate developers from the plateau, and that time using the tool alone isn’t enough to sustain peak productivity gains.

What might be causing the plateau?

Before exploring potential causes, an important caveat to mention is that our study covers a limited number of quarters, so the patterns we’re seeing here are early observations rather than settled conclusions. As we gather more data, we’ll be able to test these patterns over longer time horizons and revise the picture accordingly.

With that in mind, we want to offer a few possible explanations for the plateau. We can’t yet say which of these are doing the most work, or whether there are other factors we haven’t yet considered, but each is consistent with what we’re seeing in the data and worth investigating further.

System-level constraints

Individual efficiency gains create secondary challenges at the system level. While task-level coding is accelerating, the time saved is frequently redistributed into areas that are currently under-measured, such as increased experimentation, deeper architectural exploration, and quality improvements. Our prior research found that the median engineering organization sees a 7.8% increase in PR throughput from AI. Real, but more modest than headline claims would suggest, and consistent with the idea that the surrounding system is absorbing much of the individual-level efficiency. We may be observing a shift where the bottleneck moves from code production to system coordination. In some cases, engineering teams appear to be shipping faster than the surrounding product management and verification processes can support.

The task ceiling

The plateau could also be tied to a task ceiling. Early gains often come from automating high-volume, low-complexity work. Once these are optimized, developers may struggle to apply AI to more complex areas like architectural design or legacy refactoring. We need more research into specific use cases to understand if the plateau is universal or if developers who move AI “upstream” into design or “downstream” into debugging sustain higher gains.

Shifting baseline of productivity

Even when developers reach high time savings quickly, sustaining that perceived impact is difficult. One possible explanation is a new normal effect: once a developer integrates AI into their workflow, the resulting efficiency gains become the baseline. When surveyed in subsequent quarters, developers are no longer comparing their performance to a pre-AI workflow, but to their newly optimized standard.

Why this matters for engineering leaders

Individual efficiency gains are fragile. While AI coding assistants can deliver meaningful gains for a significant share of adopters, the large share of developers falling back from their peak savings suggests that time with the tool alone isn’t enough to sustain those gains. As developers produce code faster, they often hit ceilings, like slower code reviews and architectural bottlenecks, that neutralize individual gains. This suggests that the plateau in time-savings may not be a failure of the tool or the user, but a sign that the bottleneck has shifted from individual code production to team-level coordination. It highlights a need for deeper research into whether certain use cases, like complex debugging or requirements drafting yield more durable gains than the high-volume, low-complexity tasks that typically drive initial adoption spikes.

This week’s featured DevProd job openings. See more open roles here.

• Ashby is hiring an Staff Platform Engineer | Remote

• BambooHR is hiring a VP of Engineering | Utah (Hybrid)

• Cashea is hiring an Infrastructure & Developer Productivity Platform Engineering Manager | Remote

• Figma is hiring a Staff Software Engineer, Developer Experience | Remote; US

• Leidos is hiring a Platform Engineer | Remote; US

• Morgan Stanely is hiring an AI Platform Engineer - Vice President | New York

• Weave is hiring a Senior Platform Engineer, Data Infrastructure | Remote; US

That’s it for this week. Thanks for reading.

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In case you missed it, we just released the DX Annual recording library. You can watch the recordings by clicking through the Sessions page here. We’ll release recordings on the podcast soon as well, stay tuned.

There are several assumptions about AI circulating in executive teams and boardrooms that are shaping strategy today. For example, some leaders believe AI will ultimately mean fewer developers, or at least fewer junior engineers. Some believe AI adoption must be mandated from the top in order to take hold.

These beliefs are rarely black and white. So at DX Annual, we convened a panel of senior engineering and research leaders to debate these questions in the open. Our aim was to surface the nuance behind these assumptions, give our audience exposure to contrasting viewpoints, and equip them to articulate their own position with greater clarity and confidence.

The panel:

• Rafe Colburn, Chief Product and Technology Officer at Etsy, where he’s led engineering for 14 years through multiple waves of technology change

• Jesse Adametz, Sr. Director of Engineering, Platform Engineering at Twilio, overseeing developer tooling and productivity for thousands of engineers

• Eirini Kalliamvakou, Research Advisor at GitHub, whose work on developer productivity measurement has shaped how the industry thinks about engineering effectiveness

• Collin Green, Senior Staff UX Researcher at Google, where he studies developer workflows and the factors that drive or block productivity

• Brian Houck, coauthor of the SPACE framework (and now Distinguished Researcher at DX), whose research on AI’s impact on engineering spans many thousands of developers and managers

Each panelist was read a series of statements and asked for their opinion. Below is a recap of each statement and the panelists’ reactions. You can watch the full session here.

Will AI mean fewer engineers?

Statement: An AI-first SDLC means fewer engineers.

Rafe: Thumbs down. A job is a bundle of tasks, and AI is a clean substitute for some of them. People might not be doing those things anymore. But the demand for software isn’t going away, it’s going up. The price of building software is going to go down. So I don’t think fewer engineers is a likely scenario in the near future. I bet against it.

Brian: I agree, it’s not about fewer engineers, it’s about doing more. But one nuance: in the future, what do we even define as a software engineer? The bundle of tasks we think of today that make up a software engineer may change. Writing code is a core part of the identity. The people who do that specific thing may go down, but the total number of makers and builders won’t.

Collin: You’ll probably be able to do the same amount of stuff with fewer people, but that doesn’t mean demand will go down. Small companies that want to accomplish something will be able to get over the barrier to entry faster. It may actually increase demand for engineering skills.

Is AI accelerating technical debt?

Statement: AI is currently creating technical debt faster than it is helping us refactor it.

Eirini: Because you said “currently,” yes. Things with code generation are moving at a pace where the rest of the system hasn’t evolved enough to match it. We’re also seeing the anticipation of technical debt throttling how much developers use agents. They’re trying to balance the risk of something with the velocity of something, and it’s mental math that has to happen in the moment. There are ways to manage against the accumulation, like event-driven or scheduled agents that do maintenance and hygiene for codebases. It’s not a perfect solution, but it’s working for now. The accumulation of technical debt is certainly a big issue.

Jesse: It’s too definitive of a statement. We look at our engineers in quartiles, and our highest-performing engineers are also our highest-performing AI engineers. AI is an amplifier. If you’ve got high-performing engineers, they weren’t putting garbage into the system before and they’re still not. It’s garbage in, garbage out.

Rafe: I’ve worked at Etsy for 14 years. Proportionally, are we creating more tech debt now than five or ten years ago? No. The old code is still there. We’re creating a greater volume of code, but I’m not sure a greater proportion of it is tech debt. Automated code reviews are helping, and you can ask a coding agent to look at patterns in the code. The greater volume probably brings more tech debt in absolute terms, but I don’t think it’s an accelerator for tech debt at a greater rate than it’s an accelerator for anything else.

Brian: Disagree with the others. Organizations are optimizing for PR velocity, not cleanliness. And cognitive debt, understanding our systems less as AI writes more of the code, is a form of technical debt that’s growing. In the short term, tech debt is going up relative to PR velocity.

Collin: Technical debt is a business decision, not strictly an engineering one. If businesses have the same tolerance for risk and the same market pressures, they’ll incur the same total amount. But viewing agentic development as low-cost increases the risk of imprudent decisions.

Eirini: The choice of what even are you building becomes so important.

Will most code be AI-generated within five years?

Statement: In five years, more than 50% of code in most organizations will be written by AI.

Collin: If we’re talking about new code, yes, maybe even sooner than five years. But it’s worth asking whether that’s code that would have been written by humans otherwise, or just a larger total volume. Also worth considering how much of it will be rapidly rewritten.

Jesse: I interpreted the statement differently. If the claim is 50% of all code in a codebase, that’s wild. Too many companies have too much legacy software, and there’s no advantage to rewriting all of it.

Rafe: Agreed it has to be new code for the statement to hold.

Is the future of engineering about managing agents?

Statement: The future of software engineering is more about managing agents rather than about writing code.

Brian: In an idealized world, yes. But humans aren’t great at multitasking. Microsoft’s internal research shows that even experienced developers under time pressure revert to sequential, single-threaded agentic workflows. People with prior management experience (delegation, context-switching) will be more successful, but it’s a different skillset that we’re not hiring for yet.

Rafe: Thumbs up, loosely. Even using Claude Code today, you’re already managing multiple agents, it’s just abstracted away. The hobby-project vision of orchestrating 15 agents simultaneously isn’t the future. AI will help manage that complexity. But will a large chunk of your work be mediated by at least one agent? Yes. Agent orchestration is not a full-time human job.

Jesse: As a director, my brain naturally goes to managing agents. But the identity of a software engineer matters. Not everyone wants to be a manager, and not everyone is good at it. A blanket statement that everyone will manage agents is too much.

Eirini: I interpreted “managing” as the real engineering work of setting agents up for success: defining intent, setting constraints and guardrails, providing context, and then verifying output. That’s real engineering at a different level of abstraction. If that’s what the statement means, it’s a strong thumbs up.

Collin: Micromanaging agents is a failure mode. That leads to task-switching bottlenecks.

Do leaders need to mandate AI adoption?

Statement: Leaders need to mandate AI usage to make sure adoption is moving along.

Jesse: Not what we’ve seen. Twilio did light enablement (install defaults, basic guidance), and adoption took off on its own. No top-down mandate needed.

Rafe: People outside of engineering orgs often push for mandates because they’re afraid of falling behind. But mandates lead to shallow adoption and “tokenmaxxing.” AI adoption is inevitable, so why mandate something inevitable? If someone had said a year or two ago that every single talk at the DX conference would be about AI, people would have said no way—but is there anything else to talk about? Telling software engineers AI is going to be part of their job just feels like you’re fighting last year’s battle this year. Leaders should focus on removing friction and making it easy to create value, not pushing people.

Brian: I’m running a study of ~600 engineers and managers. The biggest disagreement: the majority of engineering managers think AI usage is a reasonable individual performance metric. Engineers disagree. That’s a myth to dispel. Activity metrics are useful for understanding patterns, but should not be used as direct performance measures.

Jesse: If organizations aren’t crystal clear about how AI will be measured, people will fill the gap with their biggest anxieties. Twilio isn’t putting AI usage in career frameworks anytime soon, but the reality is you’re compared to your peers.

Rafe: If you went to a job interview today and said “I don’t use AI coding tools,” it would be like saying you’re a programmer who refuses to use a text editor. It would just sound strange. Adoption is inevitable without a mandate.

Is code review now the bottleneck?

Statement: The bottleneck of software delivery is no longer writing code, it’s now reviewing code.

Collin: As Brian noted earlier in the day, developers only spend about 14% of their time writing code, so code was never really the bottleneck. The real bottlenecks are decision-making, prioritization, product design, support, and operations.

Jesse: AI is an amplifier. Whatever your organization’s bottleneck used to be, it still is. For Twilio, deep work has been an issue for a long time and still is. The conversation has shifted to “agent experience,” but the fix is the same: improve developer experience. Meetings, coordination tax, decision-making, these have always been the problems.

Brian: Code review is still a bottleneck, and it’s increasing as more code is produced. But at Microsoft, some features take two or more years from planning to customer delivery. Going from two days to three days on review isn’t the long pole. Planning and prioritization are.

Rafe: The perceived bottleneck of code review is what’s interesting. When writing a PR took two days, a review delay was annoying but tolerable. When writing takes 10 minutes, the human process of finding a reviewer and waiting feels unbearable. The interruptions of human processes, unless they provide clear value, are grating.

Jesse: Twilio’s data showed their highest-quartile AI developers took a ~14-point hit in perceived code review turnaround, but their actual median merge time decreased by multiple hours. It’s pure perception.

Is risk the only thing holding back adoption?

Statement: The only thing holding engineers back on AI adoption is unnecessary worry about risk.

Eirini: Engineers feel deeply accountable for what ships. When something is high-risk or hard to undo, they pull back on how much they leverage agentic velocity. That’s not irrational, it’s human behavior that’s hard to get over.

Brian: Risk is a big reason, but not the only one. Over a fifth of developers in our research cited concern about introducing defects and vulnerabilities as a top barrier.

Jesse: I question whether AI is actually increasing risk, since engineers were already introducing bugs before AI.

Rafe: The risk landscape has completely shifted. If we had a butter knife and now you have a chainsaw, the risks are different. The risks that you won’t be able to cut down a tree have gone way, way down. But the risks that you might really make a mess are a lot higher. We have people using these tools who don’t really know how a chainsaw is different than a butter knife — “Look, someone just gave me something, cool.” And on the other hand, engineers who are like, “I don’t want to use the chainsaw unless I really understand everything about how it works.”

Collin: Beyond risk, there are basic enablement problems: helping people choose tools, configure them, and get started. Those aren’t AI-specific, they’re just adoption problems.

Are AI adoption problems really culture problems?

Statement: Most AI adoption problems are really culture and management problems, not tooling problems.

Rafe: Culture and management problems are real, but sometimes the next blocker is a tooling problem, or a process problem. Process is a form of tooling. At Etsy, with a 20-year-old codebase and deeply tenured engineers, stated and unstated processes create friction. It’s all of the above. But if you’re a leader and you’re not giving people time to learn, you are doing it wrong. I used to ask engineers, “Are you working as fast as you can?” “I don’t know.” “Are you learning anything from what you’re doing?” “No.” Then you’re working as fast as you can. You’re not taking any time for that. Making space for learning solves 90% of these problems.

Collin: Most problems are human problems. Tool impediments exist, but the bigger barriers are incentives, anxiety, learning, and space to learn. Leaders who give people time to learn demonstrate they understand how the world actually works.

Eirini: The transformation is massive. Bottlenecks have shifted, processes aren’t working as expected, metrics no longer mean the same thing, and people are having an identity crisis. It can’t be just a tooling problem. Culture, motivation, and infrastructure all need to adjust.

Jesse: There’s a compounding effect. For people who haven’t started, it’s getting harder to start. I told my VP a couple weeks ago, “I have to quit to be able to keep up with the industry.” That’s the day job. So if folks haven’t gotten started, I can only imagine how much more daunting it’s getting.

Brian: Everything is moving so fast that what you learned yesterday is irrelevant tomorrow. Long-term burnout and fear of falling behind are basic human challenges. No matter how complex the technology, it’s always a human problem at the end of the day.

Eirini: Teams that did two-week group learning sprints (everyone uses agents for everything, no exceptions) saw much better results in adoption, engagement, and outcomes than teams where individuals learned on their own.

—

There’s a common throughline in these discussions: AI is reshaping the task mix of software work, but not eliminating the need for engineers. Additionally, the biggest risks and opportunities sit in how leaders design roles, measure impact, and consider the full PDLC, not in whether AI can generate more code.

To go deeper, you can watch the full session here and explore the rest of the DX Annual recordings on the Sessions page.

This week’s featured DevProd job openings. See more open roles here.

• Ashby is hiring an Staff Platform Engineer | Remote

• BambooHR is hiring a VP of Engineering | Utah (Hybrid)

• Cashea is hiring an Infrastructure & Developer Productivity Platform Engineering Manager | Remote

• Figma is hiring a Staff Software Engineer, Developer Experience | Remote; US

• Leidos is hiring a Platform Engineer | Remote; US

• Morgan Stanely is hiring an AI Platform Engineer - Vice President | New York

• Weave is hiring a Senior Platform Engineer, Data Infrastructure | Remote; US

That’s it for this week. Thanks for reading.

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