Jul 11, 2025
How AI is Transforming Engineering Team Dynamics
Conor Bronsdon
Head of Developer Awareness
Conor Bronsdon
Head of Developer Awareness
Artificial Intelligence (AI) is reshaping engineering beyond just code generation. It's fundamentally shifting product development and team collaboration.
In a Chain of Thought podcast, Conor Bronsdon, Head of Developer Awareness at Galileo, sat down with Charity Majors, co-founder and CTO of Honeycomb, to discuss critical insights about modern engineering priorities.
The Role of AI in Engineering Teams
AI's impact extends well beyond automating routine coding tasks. As engineering evolves, the measure of success increasingly focuses on solving tangible business problems rather than simply writing elegant code. Majors articulated this shift clearly: "Software engineering is not about writing code. It's about solving business problems with technology."
Transition from Code Generation to Production Focus
The introduction of sophisticated AI coding tools is accelerating a fundamental shift in engineering priorities. While the industry has historically glorified code creation, the real challenges and value exist in what happens after deployment.
"Writing code, greenfield code, is typically the fastest, easiest part of the life cycle," Majors explained, highlighting how AI is enabling engineers to refocus on the more complex aspects of system design, integration, and maintenance.
This transition represents a maturation of the field, where software's true worth manifests in production environments where users interact with systems and business value is ultimately generated. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success, a shift facilitated by the adoption of agentic AI frameworks.
Engineers must now cultivate expertise in analyzing production data, ensuring system observability, and managing complete software lifecycles—skills that extend far beyond writing algorithms or functions. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success.
Impact of AI Tools on Team Dynamics
The integration of AI tools is creating more fluid engineering environments where value derives from problems solved rather than code produced. Junior engineers can leverage these tools to accelerate their learning curves, while senior engineers can multiply their impact across increasingly complex challenges.
AI is also blurring traditional role boundaries. Everyone from developers to operations specialists now shares responsibility for production outcomes, creating cross-functional teams that approach problems holistically, highlighting the need for AI standardization.
This collaborative environment allows engineers to focus on uniquely human aspects of problem-solving—creativity, context awareness, and strategic thinking—that remain beyond AI's capabilities, even as AI agents in human interaction are revolutionizing the field.
By automating routine tasks and enhancing productivity, AI teams can concentrate on operations and problem-solving. AI serves as a support tool rather than a replacement, highlighting the distinction between short-term projects and long-term, mission-critical systems requiring ongoing human attention.
Majors stresses that teams must remain adaptable and committed to learning: "Interaction with these tools can accelerate growth, creating engineers who are not bound to repetitive tasks but engage more deeply with problem-solving and innovative solutions." AI isn't just automation; it pushes teams to think strategically about their work and objectives.
As companies navigate this transition, their success depends on how effectively they adapt and incorporate AI into their workflows, staying competitive in a rapidly changing tech environment.
The Evolving Expectations of Engineering Managers
As AI transforms technical practices, engineering managers face dramatically expanded responsibilities. Their focus must shift from overseeing code production to orchestrating human-AI collaboration while maintaining unwavering attention on business outcomes.
New Skills and Approaches Required
Today's engineering managers need a hybrid skillset combining AI fluency with enhanced people leadership. They must identify which problems benefit from AI assistance versus those requiring human creativity and judgment, while creating environments that nurture continuous learning.
"It's an apprenticeship industry, full stop," Majors emphasized, highlighting how managers must intentionally create growth opportunities as AI assumes routine tasks that traditionally provided learning experiences for junior engineers.
This includes establishing frameworks for ethical AI use and fostering psychological safety where team members can experiment without fearing obsolescence.
Successful managers translate between technical possibilities and business objectives, helping teams navigate the complex interplay between human creativity and machine efficiency. They employ human evaluation metrics to assess AI system performance and alignment with business goals. Their focus remains on delivering genuine business value rather than pursuing technical novelty for its own sake.
Responsibilities of Engineering Managers
Engineering managers must now serve as stewards of production excellence while architecting effective human-AI collaboration. "The center of gravity is shifting from preproduction to postproduction," Majors observed, requiring managers to prioritize observability and production insights in team processes.
This expansion of responsibilities includes implementing monitoring systems that provide visibility into AI-enhanced code performance in real-world conditions and ensuring AI accuracy and AI functional correctness. Managers must address emerging challenges like cognitive drift, where engineers lose situational awareness due to increased automation, by maintaining human understanding of systems.
Perhaps most importantly, engineering managers must advocate for team diversity and junior talent development. "We have to represent...we have to go to bat for junior engineers because they don't have the power yet to go to bat for themselves," Majors stressed.
This advocacy ensures diverse perspectives in environments where AI might otherwise favor experienced engineers' workflows, creating balanced teams that combine AI efficiency with human creativity.
Strategies for Integrating Production Insights into Team Culture
Creating a production-centered culture requires deliberate strategies that shift attention from code creation to real-world outcomes. High-performing teams distinguish themselves through their intense focus on production environments rather than development sandboxes.
Fostering a Production-Focused Culture
Building this culture demands structural changes in work organization—prioritizing production metrics in planning, celebrating operational excellence over feature velocity, and redefining success around business impact rather than technical elegance. Engineers must develop a holistic understanding of socio-technical systems, recognizing that challenges span both code quality and human contexts.
Leaders should implement mechanisms connecting engineers directly with their work, such as placing developers on-call for systems they build or incorporating user feedback into daily standups. This approach transforms engineers into what Majors describes as "the value-generating engines of modern businesses," measured by problems solved rather than code produced.
Organizations successfully fostering this culture typically experience improved system reliability, faster incident resolution, and innovative solutions addressing underlying business needs rather than technical symptoms.
Production as a Feedback Tool
Production environments offer real-time insights impossible to simulate in testing contexts. When engineers regularly analyze production data, they develop an intuitive understanding of system behavior under actual conditions—what Majors calls "the glasses" of observability, transforming theoretical knowledge into practical wisdom.
Effective teams establish structured pathways for production insights to flow back into development, using techniques like feature flagging and canary deployments to gather live feedback while minimizing risk. Regular production data reviews become learning opportunities, with teams analyzing not just failures but also unexpected user behaviors and performance patterns.
As engineers grow comfortable with production feedback, they naturally shift from reactive troubleshooting to proactive system design that anticipates real-world challenges. Mature organizations integrate these insights not just into technical practices but into strategic planning, allowing actual usage patterns to inform roadmap priorities.
Continuous Learning and Improvement
A culture of continuous learning forms the foundation of production-focused teams. When engineers engage deeply with production systems, each deployment and incident becomes a valuable learning opportunity rather than a crisis.
Majors advocates for blameless post-mortems that extract maximum insights from production issues, transforming failures into organizational wisdom. This requires psychological safety where engineers can admit mistakes without fear of repercussions.
"If you hate AI, you should really embrace it...the more you hate it, the more you should engage with it," Majors provocatively suggested, highlighting how confronting challenges directly leads to growth. Effective leaders recognize and reward learning behaviors like thorough incident documentation, knowledge sharing, and mentoring.
The most resilient organizations view learning not as an occasional activity but as their core competitive advantage—a continuous process embedded in daily work that prepares teams for increasingly complex challenges as technology and business needs evolve.
Engineering Excellence in an AI-Augmented Future
As we look toward the horizon of engineering practice, the conversation now points to a future where human ingenuity and AI capabilities form a powerful symbiosis. Teams that successfully navigate this transition will likely develop entirely new workflows that leverage AI as a true collaborator rather than merely a tool, enabling engineers to tackle increasingly complex business challenges that were previously beyond reach.
The next evolution of engineering excellence will center production intuition – a deeply developed sense for how systems behave in the real world that combines human judgment with AI-enhanced observability.
Organizations that cultivate this hybrid intelligence approach stand to create unprecedented value, with engineers focusing their cognitive resources on system design, user experience, and business strategy while AI handles increasingly sophisticated implementation details.
Galileo stands at the forefront of helping businesses navigate this transformation successfully. Explore Galileo to ensure your AI applications perform optimally while maintaining security and reliability. Dedicated to advancing generative AI implementation, Galileo guides enterprises toward a future where AI seamlessly integrates into business strategies to tackle complex challenges efficiently.
Listen to the complete Chain of Thought podcast conversation for invaluable perspectives on nurturing junior talent in an AI-augmented landscape, concrete strategies for improving production observability, and thought-provoking reflections on engineering's evolving identity – essential knowledge for any leader navigating this rapidly changing terrain.
For more insights on Generative AI, check other Chain of Thought episodes, where software engineers and AI leaders share stories, strategies, and practical techniques.
Artificial Intelligence (AI) is reshaping engineering beyond just code generation. It's fundamentally shifting product development and team collaboration.
In a Chain of Thought podcast, Conor Bronsdon, Head of Developer Awareness at Galileo, sat down with Charity Majors, co-founder and CTO of Honeycomb, to discuss critical insights about modern engineering priorities.
The Role of AI in Engineering Teams
AI's impact extends well beyond automating routine coding tasks. As engineering evolves, the measure of success increasingly focuses on solving tangible business problems rather than simply writing elegant code. Majors articulated this shift clearly: "Software engineering is not about writing code. It's about solving business problems with technology."
Transition from Code Generation to Production Focus
The introduction of sophisticated AI coding tools is accelerating a fundamental shift in engineering priorities. While the industry has historically glorified code creation, the real challenges and value exist in what happens after deployment.
"Writing code, greenfield code, is typically the fastest, easiest part of the life cycle," Majors explained, highlighting how AI is enabling engineers to refocus on the more complex aspects of system design, integration, and maintenance.
This transition represents a maturation of the field, where software's true worth manifests in production environments where users interact with systems and business value is ultimately generated. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success, a shift facilitated by the adoption of agentic AI frameworks.
Engineers must now cultivate expertise in analyzing production data, ensuring system observability, and managing complete software lifecycles—skills that extend far beyond writing algorithms or functions. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success.
Impact of AI Tools on Team Dynamics
The integration of AI tools is creating more fluid engineering environments where value derives from problems solved rather than code produced. Junior engineers can leverage these tools to accelerate their learning curves, while senior engineers can multiply their impact across increasingly complex challenges.
AI is also blurring traditional role boundaries. Everyone from developers to operations specialists now shares responsibility for production outcomes, creating cross-functional teams that approach problems holistically, highlighting the need for AI standardization.
This collaborative environment allows engineers to focus on uniquely human aspects of problem-solving—creativity, context awareness, and strategic thinking—that remain beyond AI's capabilities, even as AI agents in human interaction are revolutionizing the field.
By automating routine tasks and enhancing productivity, AI teams can concentrate on operations and problem-solving. AI serves as a support tool rather than a replacement, highlighting the distinction between short-term projects and long-term, mission-critical systems requiring ongoing human attention.
Majors stresses that teams must remain adaptable and committed to learning: "Interaction with these tools can accelerate growth, creating engineers who are not bound to repetitive tasks but engage more deeply with problem-solving and innovative solutions." AI isn't just automation; it pushes teams to think strategically about their work and objectives.
As companies navigate this transition, their success depends on how effectively they adapt and incorporate AI into their workflows, staying competitive in a rapidly changing tech environment.
The Evolving Expectations of Engineering Managers
As AI transforms technical practices, engineering managers face dramatically expanded responsibilities. Their focus must shift from overseeing code production to orchestrating human-AI collaboration while maintaining unwavering attention on business outcomes.
New Skills and Approaches Required
Today's engineering managers need a hybrid skillset combining AI fluency with enhanced people leadership. They must identify which problems benefit from AI assistance versus those requiring human creativity and judgment, while creating environments that nurture continuous learning.
"It's an apprenticeship industry, full stop," Majors emphasized, highlighting how managers must intentionally create growth opportunities as AI assumes routine tasks that traditionally provided learning experiences for junior engineers.
This includes establishing frameworks for ethical AI use and fostering psychological safety where team members can experiment without fearing obsolescence.
Successful managers translate between technical possibilities and business objectives, helping teams navigate the complex interplay between human creativity and machine efficiency. They employ human evaluation metrics to assess AI system performance and alignment with business goals. Their focus remains on delivering genuine business value rather than pursuing technical novelty for its own sake.
Responsibilities of Engineering Managers
Engineering managers must now serve as stewards of production excellence while architecting effective human-AI collaboration. "The center of gravity is shifting from preproduction to postproduction," Majors observed, requiring managers to prioritize observability and production insights in team processes.
This expansion of responsibilities includes implementing monitoring systems that provide visibility into AI-enhanced code performance in real-world conditions and ensuring AI accuracy and AI functional correctness. Managers must address emerging challenges like cognitive drift, where engineers lose situational awareness due to increased automation, by maintaining human understanding of systems.
Perhaps most importantly, engineering managers must advocate for team diversity and junior talent development. "We have to represent...we have to go to bat for junior engineers because they don't have the power yet to go to bat for themselves," Majors stressed.
This advocacy ensures diverse perspectives in environments where AI might otherwise favor experienced engineers' workflows, creating balanced teams that combine AI efficiency with human creativity.
Strategies for Integrating Production Insights into Team Culture
Creating a production-centered culture requires deliberate strategies that shift attention from code creation to real-world outcomes. High-performing teams distinguish themselves through their intense focus on production environments rather than development sandboxes.
Fostering a Production-Focused Culture
Building this culture demands structural changes in work organization—prioritizing production metrics in planning, celebrating operational excellence over feature velocity, and redefining success around business impact rather than technical elegance. Engineers must develop a holistic understanding of socio-technical systems, recognizing that challenges span both code quality and human contexts.
Leaders should implement mechanisms connecting engineers directly with their work, such as placing developers on-call for systems they build or incorporating user feedback into daily standups. This approach transforms engineers into what Majors describes as "the value-generating engines of modern businesses," measured by problems solved rather than code produced.
Organizations successfully fostering this culture typically experience improved system reliability, faster incident resolution, and innovative solutions addressing underlying business needs rather than technical symptoms.
Production as a Feedback Tool
Production environments offer real-time insights impossible to simulate in testing contexts. When engineers regularly analyze production data, they develop an intuitive understanding of system behavior under actual conditions—what Majors calls "the glasses" of observability, transforming theoretical knowledge into practical wisdom.
Effective teams establish structured pathways for production insights to flow back into development, using techniques like feature flagging and canary deployments to gather live feedback while minimizing risk. Regular production data reviews become learning opportunities, with teams analyzing not just failures but also unexpected user behaviors and performance patterns.
As engineers grow comfortable with production feedback, they naturally shift from reactive troubleshooting to proactive system design that anticipates real-world challenges. Mature organizations integrate these insights not just into technical practices but into strategic planning, allowing actual usage patterns to inform roadmap priorities.
Continuous Learning and Improvement
A culture of continuous learning forms the foundation of production-focused teams. When engineers engage deeply with production systems, each deployment and incident becomes a valuable learning opportunity rather than a crisis.
Majors advocates for blameless post-mortems that extract maximum insights from production issues, transforming failures into organizational wisdom. This requires psychological safety where engineers can admit mistakes without fear of repercussions.
"If you hate AI, you should really embrace it...the more you hate it, the more you should engage with it," Majors provocatively suggested, highlighting how confronting challenges directly leads to growth. Effective leaders recognize and reward learning behaviors like thorough incident documentation, knowledge sharing, and mentoring.
The most resilient organizations view learning not as an occasional activity but as their core competitive advantage—a continuous process embedded in daily work that prepares teams for increasingly complex challenges as technology and business needs evolve.
Engineering Excellence in an AI-Augmented Future
As we look toward the horizon of engineering practice, the conversation now points to a future where human ingenuity and AI capabilities form a powerful symbiosis. Teams that successfully navigate this transition will likely develop entirely new workflows that leverage AI as a true collaborator rather than merely a tool, enabling engineers to tackle increasingly complex business challenges that were previously beyond reach.
The next evolution of engineering excellence will center production intuition – a deeply developed sense for how systems behave in the real world that combines human judgment with AI-enhanced observability.
Organizations that cultivate this hybrid intelligence approach stand to create unprecedented value, with engineers focusing their cognitive resources on system design, user experience, and business strategy while AI handles increasingly sophisticated implementation details.
Galileo stands at the forefront of helping businesses navigate this transformation successfully. Explore Galileo to ensure your AI applications perform optimally while maintaining security and reliability. Dedicated to advancing generative AI implementation, Galileo guides enterprises toward a future where AI seamlessly integrates into business strategies to tackle complex challenges efficiently.
Listen to the complete Chain of Thought podcast conversation for invaluable perspectives on nurturing junior talent in an AI-augmented landscape, concrete strategies for improving production observability, and thought-provoking reflections on engineering's evolving identity – essential knowledge for any leader navigating this rapidly changing terrain.
For more insights on Generative AI, check other Chain of Thought episodes, where software engineers and AI leaders share stories, strategies, and practical techniques.
Artificial Intelligence (AI) is reshaping engineering beyond just code generation. It's fundamentally shifting product development and team collaboration.
In a Chain of Thought podcast, Conor Bronsdon, Head of Developer Awareness at Galileo, sat down with Charity Majors, co-founder and CTO of Honeycomb, to discuss critical insights about modern engineering priorities.
The Role of AI in Engineering Teams
AI's impact extends well beyond automating routine coding tasks. As engineering evolves, the measure of success increasingly focuses on solving tangible business problems rather than simply writing elegant code. Majors articulated this shift clearly: "Software engineering is not about writing code. It's about solving business problems with technology."
Transition from Code Generation to Production Focus
The introduction of sophisticated AI coding tools is accelerating a fundamental shift in engineering priorities. While the industry has historically glorified code creation, the real challenges and value exist in what happens after deployment.
"Writing code, greenfield code, is typically the fastest, easiest part of the life cycle," Majors explained, highlighting how AI is enabling engineers to refocus on the more complex aspects of system design, integration, and maintenance.
This transition represents a maturation of the field, where software's true worth manifests in production environments where users interact with systems and business value is ultimately generated. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success, a shift facilitated by the adoption of agentic AI frameworks.
Engineers must now cultivate expertise in analyzing production data, ensuring system observability, and managing complete software lifecycles—skills that extend far beyond writing algorithms or functions. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success.
Impact of AI Tools on Team Dynamics
The integration of AI tools is creating more fluid engineering environments where value derives from problems solved rather than code produced. Junior engineers can leverage these tools to accelerate their learning curves, while senior engineers can multiply their impact across increasingly complex challenges.
AI is also blurring traditional role boundaries. Everyone from developers to operations specialists now shares responsibility for production outcomes, creating cross-functional teams that approach problems holistically, highlighting the need for AI standardization.
This collaborative environment allows engineers to focus on uniquely human aspects of problem-solving—creativity, context awareness, and strategic thinking—that remain beyond AI's capabilities, even as AI agents in human interaction are revolutionizing the field.
By automating routine tasks and enhancing productivity, AI teams can concentrate on operations and problem-solving. AI serves as a support tool rather than a replacement, highlighting the distinction between short-term projects and long-term, mission-critical systems requiring ongoing human attention.
Majors stresses that teams must remain adaptable and committed to learning: "Interaction with these tools can accelerate growth, creating engineers who are not bound to repetitive tasks but engage more deeply with problem-solving and innovative solutions." AI isn't just automation; it pushes teams to think strategically about their work and objectives.
As companies navigate this transition, their success depends on how effectively they adapt and incorporate AI into their workflows, staying competitive in a rapidly changing tech environment.
The Evolving Expectations of Engineering Managers
As AI transforms technical practices, engineering managers face dramatically expanded responsibilities. Their focus must shift from overseeing code production to orchestrating human-AI collaboration while maintaining unwavering attention on business outcomes.
New Skills and Approaches Required
Today's engineering managers need a hybrid skillset combining AI fluency with enhanced people leadership. They must identify which problems benefit from AI assistance versus those requiring human creativity and judgment, while creating environments that nurture continuous learning.
"It's an apprenticeship industry, full stop," Majors emphasized, highlighting how managers must intentionally create growth opportunities as AI assumes routine tasks that traditionally provided learning experiences for junior engineers.
This includes establishing frameworks for ethical AI use and fostering psychological safety where team members can experiment without fearing obsolescence.
Successful managers translate between technical possibilities and business objectives, helping teams navigate the complex interplay between human creativity and machine efficiency. They employ human evaluation metrics to assess AI system performance and alignment with business goals. Their focus remains on delivering genuine business value rather than pursuing technical novelty for its own sake.
Responsibilities of Engineering Managers
Engineering managers must now serve as stewards of production excellence while architecting effective human-AI collaboration. "The center of gravity is shifting from preproduction to postproduction," Majors observed, requiring managers to prioritize observability and production insights in team processes.
This expansion of responsibilities includes implementing monitoring systems that provide visibility into AI-enhanced code performance in real-world conditions and ensuring AI accuracy and AI functional correctness. Managers must address emerging challenges like cognitive drift, where engineers lose situational awareness due to increased automation, by maintaining human understanding of systems.
Perhaps most importantly, engineering managers must advocate for team diversity and junior talent development. "We have to represent...we have to go to bat for junior engineers because they don't have the power yet to go to bat for themselves," Majors stressed.
This advocacy ensures diverse perspectives in environments where AI might otherwise favor experienced engineers' workflows, creating balanced teams that combine AI efficiency with human creativity.
Strategies for Integrating Production Insights into Team Culture
Creating a production-centered culture requires deliberate strategies that shift attention from code creation to real-world outcomes. High-performing teams distinguish themselves through their intense focus on production environments rather than development sandboxes.
Fostering a Production-Focused Culture
Building this culture demands structural changes in work organization—prioritizing production metrics in planning, celebrating operational excellence over feature velocity, and redefining success around business impact rather than technical elegance. Engineers must develop a holistic understanding of socio-technical systems, recognizing that challenges span both code quality and human contexts.
Leaders should implement mechanisms connecting engineers directly with their work, such as placing developers on-call for systems they build or incorporating user feedback into daily standups. This approach transforms engineers into what Majors describes as "the value-generating engines of modern businesses," measured by problems solved rather than code produced.
Organizations successfully fostering this culture typically experience improved system reliability, faster incident resolution, and innovative solutions addressing underlying business needs rather than technical symptoms.
Production as a Feedback Tool
Production environments offer real-time insights impossible to simulate in testing contexts. When engineers regularly analyze production data, they develop an intuitive understanding of system behavior under actual conditions—what Majors calls "the glasses" of observability, transforming theoretical knowledge into practical wisdom.
Effective teams establish structured pathways for production insights to flow back into development, using techniques like feature flagging and canary deployments to gather live feedback while minimizing risk. Regular production data reviews become learning opportunities, with teams analyzing not just failures but also unexpected user behaviors and performance patterns.
As engineers grow comfortable with production feedback, they naturally shift from reactive troubleshooting to proactive system design that anticipates real-world challenges. Mature organizations integrate these insights not just into technical practices but into strategic planning, allowing actual usage patterns to inform roadmap priorities.
Continuous Learning and Improvement
A culture of continuous learning forms the foundation of production-focused teams. When engineers engage deeply with production systems, each deployment and incident becomes a valuable learning opportunity rather than a crisis.
Majors advocates for blameless post-mortems that extract maximum insights from production issues, transforming failures into organizational wisdom. This requires psychological safety where engineers can admit mistakes without fear of repercussions.
"If you hate AI, you should really embrace it...the more you hate it, the more you should engage with it," Majors provocatively suggested, highlighting how confronting challenges directly leads to growth. Effective leaders recognize and reward learning behaviors like thorough incident documentation, knowledge sharing, and mentoring.
The most resilient organizations view learning not as an occasional activity but as their core competitive advantage—a continuous process embedded in daily work that prepares teams for increasingly complex challenges as technology and business needs evolve.
Engineering Excellence in an AI-Augmented Future
As we look toward the horizon of engineering practice, the conversation now points to a future where human ingenuity and AI capabilities form a powerful symbiosis. Teams that successfully navigate this transition will likely develop entirely new workflows that leverage AI as a true collaborator rather than merely a tool, enabling engineers to tackle increasingly complex business challenges that were previously beyond reach.
The next evolution of engineering excellence will center production intuition – a deeply developed sense for how systems behave in the real world that combines human judgment with AI-enhanced observability.
Organizations that cultivate this hybrid intelligence approach stand to create unprecedented value, with engineers focusing their cognitive resources on system design, user experience, and business strategy while AI handles increasingly sophisticated implementation details.
Galileo stands at the forefront of helping businesses navigate this transformation successfully. Explore Galileo to ensure your AI applications perform optimally while maintaining security and reliability. Dedicated to advancing generative AI implementation, Galileo guides enterprises toward a future where AI seamlessly integrates into business strategies to tackle complex challenges efficiently.
Listen to the complete Chain of Thought podcast conversation for invaluable perspectives on nurturing junior talent in an AI-augmented landscape, concrete strategies for improving production observability, and thought-provoking reflections on engineering's evolving identity – essential knowledge for any leader navigating this rapidly changing terrain.
For more insights on Generative AI, check other Chain of Thought episodes, where software engineers and AI leaders share stories, strategies, and practical techniques.
Artificial Intelligence (AI) is reshaping engineering beyond just code generation. It's fundamentally shifting product development and team collaboration.
In a Chain of Thought podcast, Conor Bronsdon, Head of Developer Awareness at Galileo, sat down with Charity Majors, co-founder and CTO of Honeycomb, to discuss critical insights about modern engineering priorities.
The Role of AI in Engineering Teams
AI's impact extends well beyond automating routine coding tasks. As engineering evolves, the measure of success increasingly focuses on solving tangible business problems rather than simply writing elegant code. Majors articulated this shift clearly: "Software engineering is not about writing code. It's about solving business problems with technology."
Transition from Code Generation to Production Focus
The introduction of sophisticated AI coding tools is accelerating a fundamental shift in engineering priorities. While the industry has historically glorified code creation, the real challenges and value exist in what happens after deployment.
"Writing code, greenfield code, is typically the fastest, easiest part of the life cycle," Majors explained, highlighting how AI is enabling engineers to refocus on the more complex aspects of system design, integration, and maintenance.
This transition represents a maturation of the field, where software's true worth manifests in production environments where users interact with systems and business value is ultimately generated. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success, a shift facilitated by the adoption of agentic AI frameworks.
Engineers must now cultivate expertise in analyzing production data, ensuring system observability, and managing complete software lifecycles—skills that extend far beyond writing algorithms or functions. The industry is evolving from a craft-focused approach toward an outcome-driven discipline where production excellence defines success.
Impact of AI Tools on Team Dynamics
The integration of AI tools is creating more fluid engineering environments where value derives from problems solved rather than code produced. Junior engineers can leverage these tools to accelerate their learning curves, while senior engineers can multiply their impact across increasingly complex challenges.
AI is also blurring traditional role boundaries. Everyone from developers to operations specialists now shares responsibility for production outcomes, creating cross-functional teams that approach problems holistically, highlighting the need for AI standardization.
This collaborative environment allows engineers to focus on uniquely human aspects of problem-solving—creativity, context awareness, and strategic thinking—that remain beyond AI's capabilities, even as AI agents in human interaction are revolutionizing the field.
By automating routine tasks and enhancing productivity, AI teams can concentrate on operations and problem-solving. AI serves as a support tool rather than a replacement, highlighting the distinction between short-term projects and long-term, mission-critical systems requiring ongoing human attention.
Majors stresses that teams must remain adaptable and committed to learning: "Interaction with these tools can accelerate growth, creating engineers who are not bound to repetitive tasks but engage more deeply with problem-solving and innovative solutions." AI isn't just automation; it pushes teams to think strategically about their work and objectives.
As companies navigate this transition, their success depends on how effectively they adapt and incorporate AI into their workflows, staying competitive in a rapidly changing tech environment.
The Evolving Expectations of Engineering Managers
As AI transforms technical practices, engineering managers face dramatically expanded responsibilities. Their focus must shift from overseeing code production to orchestrating human-AI collaboration while maintaining unwavering attention on business outcomes.
New Skills and Approaches Required
Today's engineering managers need a hybrid skillset combining AI fluency with enhanced people leadership. They must identify which problems benefit from AI assistance versus those requiring human creativity and judgment, while creating environments that nurture continuous learning.
"It's an apprenticeship industry, full stop," Majors emphasized, highlighting how managers must intentionally create growth opportunities as AI assumes routine tasks that traditionally provided learning experiences for junior engineers.
This includes establishing frameworks for ethical AI use and fostering psychological safety where team members can experiment without fearing obsolescence.
Successful managers translate between technical possibilities and business objectives, helping teams navigate the complex interplay between human creativity and machine efficiency. They employ human evaluation metrics to assess AI system performance and alignment with business goals. Their focus remains on delivering genuine business value rather than pursuing technical novelty for its own sake.
Responsibilities of Engineering Managers
Engineering managers must now serve as stewards of production excellence while architecting effective human-AI collaboration. "The center of gravity is shifting from preproduction to postproduction," Majors observed, requiring managers to prioritize observability and production insights in team processes.
This expansion of responsibilities includes implementing monitoring systems that provide visibility into AI-enhanced code performance in real-world conditions and ensuring AI accuracy and AI functional correctness. Managers must address emerging challenges like cognitive drift, where engineers lose situational awareness due to increased automation, by maintaining human understanding of systems.
Perhaps most importantly, engineering managers must advocate for team diversity and junior talent development. "We have to represent...we have to go to bat for junior engineers because they don't have the power yet to go to bat for themselves," Majors stressed.
This advocacy ensures diverse perspectives in environments where AI might otherwise favor experienced engineers' workflows, creating balanced teams that combine AI efficiency with human creativity.
Strategies for Integrating Production Insights into Team Culture
Creating a production-centered culture requires deliberate strategies that shift attention from code creation to real-world outcomes. High-performing teams distinguish themselves through their intense focus on production environments rather than development sandboxes.
Fostering a Production-Focused Culture
Building this culture demands structural changes in work organization—prioritizing production metrics in planning, celebrating operational excellence over feature velocity, and redefining success around business impact rather than technical elegance. Engineers must develop a holistic understanding of socio-technical systems, recognizing that challenges span both code quality and human contexts.
Leaders should implement mechanisms connecting engineers directly with their work, such as placing developers on-call for systems they build or incorporating user feedback into daily standups. This approach transforms engineers into what Majors describes as "the value-generating engines of modern businesses," measured by problems solved rather than code produced.
Organizations successfully fostering this culture typically experience improved system reliability, faster incident resolution, and innovative solutions addressing underlying business needs rather than technical symptoms.
Production as a Feedback Tool
Production environments offer real-time insights impossible to simulate in testing contexts. When engineers regularly analyze production data, they develop an intuitive understanding of system behavior under actual conditions—what Majors calls "the glasses" of observability, transforming theoretical knowledge into practical wisdom.
Effective teams establish structured pathways for production insights to flow back into development, using techniques like feature flagging and canary deployments to gather live feedback while minimizing risk. Regular production data reviews become learning opportunities, with teams analyzing not just failures but also unexpected user behaviors and performance patterns.
As engineers grow comfortable with production feedback, they naturally shift from reactive troubleshooting to proactive system design that anticipates real-world challenges. Mature organizations integrate these insights not just into technical practices but into strategic planning, allowing actual usage patterns to inform roadmap priorities.
Continuous Learning and Improvement
A culture of continuous learning forms the foundation of production-focused teams. When engineers engage deeply with production systems, each deployment and incident becomes a valuable learning opportunity rather than a crisis.
Majors advocates for blameless post-mortems that extract maximum insights from production issues, transforming failures into organizational wisdom. This requires psychological safety where engineers can admit mistakes without fear of repercussions.
"If you hate AI, you should really embrace it...the more you hate it, the more you should engage with it," Majors provocatively suggested, highlighting how confronting challenges directly leads to growth. Effective leaders recognize and reward learning behaviors like thorough incident documentation, knowledge sharing, and mentoring.
The most resilient organizations view learning not as an occasional activity but as their core competitive advantage—a continuous process embedded in daily work that prepares teams for increasingly complex challenges as technology and business needs evolve.
Engineering Excellence in an AI-Augmented Future
As we look toward the horizon of engineering practice, the conversation now points to a future where human ingenuity and AI capabilities form a powerful symbiosis. Teams that successfully navigate this transition will likely develop entirely new workflows that leverage AI as a true collaborator rather than merely a tool, enabling engineers to tackle increasingly complex business challenges that were previously beyond reach.
The next evolution of engineering excellence will center production intuition – a deeply developed sense for how systems behave in the real world that combines human judgment with AI-enhanced observability.
Organizations that cultivate this hybrid intelligence approach stand to create unprecedented value, with engineers focusing their cognitive resources on system design, user experience, and business strategy while AI handles increasingly sophisticated implementation details.
Galileo stands at the forefront of helping businesses navigate this transformation successfully. Explore Galileo to ensure your AI applications perform optimally while maintaining security and reliability. Dedicated to advancing generative AI implementation, Galileo guides enterprises toward a future where AI seamlessly integrates into business strategies to tackle complex challenges efficiently.
Listen to the complete Chain of Thought podcast conversation for invaluable perspectives on nurturing junior talent in an AI-augmented landscape, concrete strategies for improving production observability, and thought-provoking reflections on engineering's evolving identity – essential knowledge for any leader navigating this rapidly changing terrain.
For more insights on Generative AI, check other Chain of Thought episodes, where software engineers and AI leaders share stories, strategies, and practical techniques.
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon
Conor Bronsdon