How we are Thinking About Generative AI

How we are Thinking About Generative AI for Developers and Tech Learning

Packt is a global tech publisher serving developers and tech professionals (TechPros). Over the last 20 years, we have published over 8,000 books and videos, gaining deep insights into the evolving challenges tech professionals face. Recently, the rapid emergence of generative AI (GenAI) technologies like CoPilot, ChatGPT, and Gemini has transformed the tech landscape, affecting everyone from software developers to business strategists.

The rapid emergence of generative AI (GenAI) technologies like CoPilot, ChatGPT, and Gemini has transformed the tech landscape.

The rapid emergence of generative AI (GenAI) technologies like CoPilot, ChatGPT, and Gemini has transformed the tech landscape. These changes affect everyone from software developers to business strategists. The tech industry is at a critical inflection point with technology use, development, and education. At Packt, we are actively exploring generative AI's impact on the industry and TechPros' daily work and learning. Here, we outline our thoughts on how GenAI reshapes professional activities and tech learning, and our strategic responses to it.

We would love to hear your feedback on this document and your thoughts on the issues raised within it. Please do send any comments to: GenAI_feedback@packt.com.

The Impact of GenAI on TechPro Work

The rapid pace of advancement in Generative AI makes it difficult to predict, but we believe, on balance, that it is a force for good in software development. A core Packt value that we share with our TechPro users is a belief in and commitment to the power of technology for progress. Our default setting is to get on board with change.

GenAI is already changing the nature of many development jobs, but it will not mean the end of software development. We are fundamentally optimistic about the future for TechPros powered by GenAI. It will mean more, faster, better work.

This is how we at Packt see these changes:

Increased Software Production

Humanity continuously evolves, adapts, and advances, maintaining a need for more sophisticated software solutions – whether those are built on traditional software platforms or on top of AI models themselves. GenAI is already transforming the economics of supply by making engineers more productive and enabling more engineering tasks. The demand for more, better software will remain, leading to an increase in the number of professionals building, designing, adapting, and managing software.
Shifts in Software Development

Much of what engineers spend time doing can be quite generic. GenAI is beginning to automate these middle-tier, routine activities, allowing developers to focus on higher-value, more creative tasks.

This shift redistributes work in three dimensions from the center of the development stack. Work moves ‘up the stack’ into architecture, domain expertise, and design, ‘down the stack’ into complex algorithm development, infrastructure, and tooling, and outwards to the edges with specific integrations and implementations.

To meet the increased demand for software, there will be significantly more designers and implementors at those development edges, with increasing business and domain focus and specialization. There will be a continuously hard-to-meet need for deep tech engineers building the tools and infrastructure that enable this automation to operate efficiently at scale and speed. This will be seen at the hardware and firmware level as well as operating systems, cloud platforms, and the models and algorithms that modern software is built upon.
Increased Domain and Business Specialization

As GenAI moves tasks from generic operations upwards and outwards to more specialized domains, engineers will increasingly make decisions that require greater judgment and domain expertise. This will lead to a greater focus on domain experience and knowledge, and a higher value on business relationships.

GenAI also democratizes the development and management of systems, making these processes accessible to more users and transforming many jobs from direct task execution to overseeing AI agents that perform the work. This evolution could significantly expand the roles involving aspects of software design or delivery.

Impact on Tech Pro Learning

GenAI integrates automation and problem solving, leading to profound change in how TechPros learn and solve problems. We see the core changes as being:

Shift Toward Just-In-Time (JIT) Continuous Learning

Developers have always preferred to learn by doing—starting work and solving problems on the fly. GenAI makes this the only viable approach. The ROI of upfront Just-In-Case (JIC) learning, where developers research technologies that might be useful in future, declines when co-pilots can accelerate initial builds and troubleshoot during development. GenAI tools can escalate to rapid Just-in-Time [JIT] learning sprints to backfill knowledge gaps as they are discovered.

GenAI tools can help engineers to rapidly understand and work on existing complex and often undocumented code bases, again backfilling knowledge gaps JIT.
Entry Level Learning Moves to Simulated Environments

The JIT learning-by-doing model also applies to students and juniors, but the study work they do will be “as good as real.” Traditional, linear courseware will be replaced by personalized, hands-on projects in rich simulated environments. These environments provide shorter, contextual learning experiences that effectively bridge the gap between theory and practice, reducing the training load on increasingly busy senior developers.
Growth in Demand for Real World Experience and Peer Interaction

As development increasingly moves up the stack and routine tasks are automated, there is a growing need for TechPros to understand specific real-world applications of systems and solutions. Highly specific, detailed, and objective case studies with high relevance to a specific problem area and technical solution will become increasingly valuable. Demand for discussion and interaction with experienced fellow professionals to share knowledge and insights will also grow. Such authentic content not only aids learning but also enhances the training of AI models.
Authoritative and Expert Insight Remains Key

Despite the shift towards more automated and JIT learning approaches, a thorough understanding of core concepts remains crucial. Books will continue to be one of the most powerful and authoritative ways for technology originators to share their foundational knowledge. This will remain the key long-term use-case for tech books.
Continuing Need for Creator Trust and Authenticity

Gen AI enables the rapid creation of written work. In the tech publishing domain, we estimate that up to around 50% of titles in certain categories on Amazon might already be AI-generated or derived. This AI content meets certain user needs, and this proliferation will continue across store platforms. We believe that human-generated work fulfils a different user need and that there will always be value in authentic creator insight and expertise. We continue to build direct relationships with tech professionals and authors to create and publish this content.

The Future is Uncertain

How this evolves is hard to know. The pace of change both in the technology and in the landscape around it has surfaced issues with reliability, compliance, cost, and memory/reasoning limitations. GenAI technology is moving extremely fast but has serious technical challenges.

GenAI technology is moving extremely fast but has serious technical challenges.

These issues will be resolved over time, but they limit the pace of actual deployment.

A Cautious Approach to Change

The case for changing existing systems, practices, and organizational models should be approached with caution. Enterprises have a high bar for adopting core systems and the deployment phase will be long and require detailed work.
Uncertainty in Computing Platforms

It remains uncertain whether GenAI might evolve into the dominant general purpose computing platform or how it will evolve past the current transformer architecture. It may become a ubiquitous implementation layer for all services over time; we do not know. However, we share the view that this is a pivotal phase for technology and for humanity.
A Mixed Economy of the Old and the New

We see a long phase of a mixed economy of old methods and new GenAI tools. There will be pockets of rapid adoption of GenAI tooling, like we see in coding co-pilots and in application areas, such as customer service agents. However, with every deployment there will be a lot of “old style” engineering: problem solving, integrations, QA, optimization. The shifts to high level working will be gradual and not immediately noticeable.
Friction in Human Systems

Human systems inherently resist change. Individuals stick with working and learning systems with which they are comfortable. Teaching methods evolve slowly, and we see different generations working and learning in different ways. While a shift toward Just-In-Time (JIT) learning is underway, structured, long-form learning will continue to play a crucial role.
Rapid Adoption Among Developers

The pace at which individual developers have adopted co-pilots and are using GenAI for problem solving is striking. We expect these trends of grassroots, individual adoption to continue and accelerate.

How Packt is Responding

The insights gained from talking with TechPros combined with our thinking about the impact of GenAI on TechPro work and learning has resulted in these strategic initiatives:

Shift to the Edges of the Development Stack in Publishing

We are pioneering new approaches to developing and publishing real world practical case studies to answer the crucial questions: “What are people actually building with this right now?” and, “How are they actually doing it?”

What are people actually building with this right now? How are they actually doing it?

We will increase our focus on publishing specific, definitive, deep, technical books from the creators and builders of new technology to help TechPros broaden their skills across the development stack. We will continue to build the tech book canon in the era of GenAI.

License for LLM Training Responsibly

The uniquely high-quality content tech authors create has immense value for LLM training. We want to support the evolution of this technology while developing model training as a potentially valuable new channel for published content.

We want authors to get fair value and the recognition they are due, and we will pursue all agreements with partners in a pragmatic but principled way.
Use GenAI to Enable a Step Change in Content Engineering and Derived Works

GenAI tools and automations can reduce the cost and effort of keeping a title up to date as technology evolves, and of creating a rich portfolio of derived works from the initial content. We call this BODE: Build Once, Deploy Everywhere.

We are exploring exciting use-cases to increase the value of the original work, and its reach into new platforms, formats, languages, and versions.
Build Packt Models and Explore JIT

We have already delivered experimental AI agents fine-tuned on specific Packt titles. We are expanding this to topic, role, and whole-library models. We are exploring integration of the Packt corpus into co-pilots and tools to deliver workflow-embedded JIT knowledge and learning escalation.
Build Professional Memberships

Recognizing the increased value of live interactions in a post-GenAI world, we are committed to enabling Tech Professionals to engage in high-quality, trustworthy interactions with peers working on similar roles and projects.