π€ I stopped my AI content creation project
and what's next for software development in the age of AI
Dear curious mind,
Remember my AI book summaries podcast experiment? Here's an update: after 50 episodes (instead of the planned 100), I have decided to end the project. This issue dives into why, along with some bold predictions on how software development will be changed massively by generative AI.
In this issue:
π‘ Shared Insight
The Rise and Fall of My AI-Generated Book Summaries Podcast
π° AI Update
GitHub Copilot Launches Free Tier for Software Developers
Google's Gemini 2.0 Flash Thinking Makes AI Reasoning Transparent
OpenAI Shared Stunning Benchmark Results for Their Next Reasoning Model o3
π Media Recommendation
Article: TOMRA Executive Predicts Transformation of Software Developer Roles by 2026
Video: Microsoft CEO Predicts AI Agents Will Replace Traditional Software Applications
π‘ Shared Insight
The Rise and Fall of My AI-Generated Book Summaries Podcast
The excitement around AI-powered content creation is high, but where should we draw the line? My recent experiment with AI-generated book summaries realized with NotebookLM as shared in a newsletter issue from October offers some insights into this question and highlights both the potential and limitations of AI in content creation.
Originally planned for 100 days, I decided to stop the project after 50 episodes. The reasons why reveal important lessons about AI content creation.
The Initial Promise
I started the project with great enthusiasm. NotebookLM proved to be an excellent tool for making informed reading decisions or prime myself before reading a book. Besides chatting with a book, it is possible to create a podcast like conversation between two hosts about the book. The AI-generated summaries offered a convenient way to explore key concepts and decide which books deserved a full read.
The Challenges
However, several issues emerged as the project progressed:
Inconsistent Quality: The AI-generated summaries varied significantly in quality - some were engaging and insightful, while others included repetitive content or an unnatural conversation style where the hosts completed sentences in an interlocked style.
Length Management: Many episodes exceeded the target duration of 20 minutes, potentially reducing their accessibility and value for listeners, and also increased the likelihood for repetitions. This resulted in deleting and recreating audio overviews multiple times for nearly all episodes.
Production Overhead: Creating polished episodes required more work than anticipated, as I edited more than half of the episodes to remove phrases like "We are back after a short break."
The Tipping Point
The real wake-up call came when trying to list the podcast on ListenNotes, a popular podcast directory which is also used by my favorite podcast player Snipd. Their rejection due to a no-AI policy made me think about the broader implications of AI-generated content. A bit later, I found a Kaggle dataset revealing over 4,000 AI-generated podcasts, which raised inside of me the question if I really want to contribute to this flood of automated content. I decided to stop at half of the initially planned number of episodes, as this mark was already close.
The Bigger Picture
This experiment highlights a crucial debate in the AI era: Just because we can automate content creation, should we? While tools like NotebookLM remain valuable for personal use at working with various sources and extracting insights, publishing AI-generated content without significant human curation will likely not serve the best interests of content consumers.
From my perspective, AI tools are best used to augment human creativity than replace it entirely. For book exploration, NotebookLM remains a powerful tool for personal use, offering quick insights and helping make informed reading decisions, especially in chatting with the material. However, sharing the AI-generated summaries as public content did not feel right after discovering the mass of AI-generated podcasts. Nevertheless, just in case you want to listen to one of the 50 book summaries I created, here is the link to the Aidful Book Summaries podcast.
This experiment serves as a reminder that as AI capabilities expand, we must carefully consider not just what's possible, but what's valuable for our audience and the broader content ecosystem. Sometimes, choosing not to publish is as important as deciding to share.
π° AI Update
GitHub Copilot Launches Free Tier for Software Developers (@github on π)
While Copilot's new free tier is great, don't forget about solid alternatives like Zed (free) or Cursor and Windsurf (paid). But if you're a VS Code user who hasn't tried AI-assisted coding yet, this is your perfect chance to start.
Google's Gemini 2.0 Flash Thinking Makes AI Reasoning Transparent (@JeffDean on π)
The recently released Gemini 2.0 Flash model was already impressive, but with the new Thinking version, it's now even better at complex reasoning and coding challenges. In contrast to OpenAI's o1, the model shows you its real thought process, making outputs more reliable and easier to verify. Best part? You can use it right now for free in Google AI Studio.
OpenAI Shared Stunning Benchmark Results for Their Next Reasoning Model o3 (@OpenAI on π)
On the last day of the 12 Days with OpenAI releases or announcements, the first benchmark results of an early stage from their next reasoning model o3 (sidenote: there was no public o2) were shared. This model achieves results of experienced programmers and mathematicians in various benchmarks. With more test time compute, the results on logical tasks keep getting better. There is no wall.
π Media Recommendation
Article: TOMRA Executive Predicts Transformation of Software Developer Roles by 2026
Dirk Balthasar, VP and Head of Core R&D at TOMRA Sorting and my former supervisor, shares a comprehensive vision of how generative AI will transform software development by 2026 in a thought-provoking article.
Key highlights from Dirks analysis:
AI-driven development environments will replace traditional programming with natural language interactions
Open-source AI tools will reduce vendor lock-in from major cloud providers
Developer roles will evolve toward strategic oversight rather than pure coding
Developer teams will not shrink but implement and maintain more features
On-premise and private hosting solutions will be easier to create and with that gain importance, especially for critical infrastructure
My take: Having worked alongside Dirk Balthasar, I have seen firsthand his exceptional ability to navigate complex technological transitions while maintaining a clear strategic vision. While many are still debating AI's role in software development, TOMRA is already preparing for this future under his leadership. Having moved on from TOMRA, I'm cheering from the sidelines as my former colleagues advance this transformation.
Video: Microsoft CEO Predicts AI Agents Will Replace Traditional Software Applications
In a recent interview on the BG squared podcast (link to the 84-min-long full video), Microsoft CEO Satya Nadella made a bold prediction about the future of software, stating that traditional business applications will "collapse" in the emerging agent era. He expresses his vision is less than four minutes, but I recommend you to listen to the commented version by Matthew Berman.
Key points from Nadella's vision:
Business applications will be replaced by AI agents that directly interact with databases
The business logic currently built into applications will move to the AI layer
Agents will work across multiple databases without discrimination
Even fundamental tools like Excel could be replaced by AI agents writing Python code on demand
The implications are significant for the software industry:
Traditional SaaS (Software as a Service) applications may become obsolete
The role of software developers will change dramatically
Databases will remain crucial, but the interface layer will be transformed
Microsoft is already moving in this direction with Copilot integration across their products
My take: This represents a fundamental shift in how we think about software development and user interaction with data. While the transition won't happen overnight, companies need to start preparing for a future where AI agents handle most data and system interactions. This could dramatically reduce the need for traditional user interfaces and reshape the entire software industry. The companies that adapt to this new paradigm early will likely have a significant advantage.
Disclaimer: This newsletter is written with the aid of AI. I use AI as an assistant to generate and optimize the text. However, the amount of AI used varies depending on the topic and the content. I always curate and edit the text myself to ensure quality and accuracy. The opinions and views expressed in this newsletter are my own and do not necessarily reflect those of the sources or the AI models.