ChatGPT Integration with InsideSpin
As a validation of AI-augmented article writing, InsideSpin has integrated ChatGPT to help flesh out unfinished articles at the moment they are requested. If you have been a past InsideSpin user, you may have noticed not all articles are fully fleshed out. While every article has a summary, only about half are fleshed out. Decisions about what to finish has been based on user interest over the years. With this POC, ChatGPT will use the InsideSpin article summary as the basis of the prompt, and return an expanded article adding insight from its underlying model. The instances are being stored for later analysis to choose one that best represents the intent of InsideSpin which the author can work with to finalize. This is a trial of an AI-augmented approach. Email founder@insidespin.com to share your views on this or ask questions about the implementation.
Generated: 2025-07-14 11:40:43
AI for Product Teams
Over the last 30 years or so, the number of coders has grown dramatically to accommodate professional needs. Starting below a million in the US in the early 90’s it is estimated there are well over 30 million professional software engineers as we head into 2025. That count does not include the millions and millions of web development tool users managing their own needs, with little formal coding training, relying on tools such as WordPress, HubSpot, Spotify, GoDaddy, AWS to generate the templated code that is needed.
The Rise of AI Coding Tools
For anyone who has used AI coding tools like CoPilot from GitHub, it is easy to see that AI tools thrive generating code. They are largely semantic language engines after all. Given most coding languages are meant to be semantically unambiguous for a computer to execute the code properly, the sophistication AI embodies to understand and generate ambiguous spoken languages like English, is largely left unneeded.
AI coding tools, however, still face challenges such as the garbage-in/garbage-out risks. This risk is prevalent in both AI coding tools and AI chat tools like ChatGPT. Thus, the integration of AI into coding requires human operators to augment their skills to derive the desired value and potentially preserve jobs in the evolving landscape.
The Role of Product Managers
For Product managers, the essence of the Product role is the synthesis of streams of requirements (input) to create the output an Engineering team can use to economically build, and a business can take to market to generate revenue. The more unambiguous and consistent the output a Product team can produce, the more likely coders and sales teams will be able to meet the needs identified.
- Alignment: AI tools can help Product teams align their objectives with market needs.
- Consistency: By using AI to analyze data, Product managers can ensure that their outputs are consistent.
- Completeness: AI-generated artifacts can provide comprehensive analyses that cover all aspects of product requirements.
While there is a general risk of homogenization of thought and approach as we become dependent on AI, as witnessed in the finance sector with the advent of spreadsheets, the benefits for Product teams include improved alignment, consistency, and completeness of analysis from the generated artifacts produced over time.
Transforming Roles in the Age of AI
Coders and Product managers are two of the areas most ripe to be transformed through comprehensive adoption of AI. As AI tools become more prevalent, the nature of jobs will inevitably change. It is crucial for professionals in these roles to adapt and migrate their talents to areas where AI drives efficiency and effectiveness.
Key Areas of Transformation
- Skill Augmentation: Professionals will need to develop skills that complement AI capabilities, such as critical thinking and strategic analysis.
- Data Literacy: Understanding and interpreting data will become essential as AI systems generate vast amounts of information.
- Collaboration: Enhanced collaboration between Product teams and AI tools will be necessary for successful outcomes.
As organizations integrate AI tools into their workflows, Product managers and coders must embrace a culture of continuous learning. This involves not only understanding how to use AI tools effectively but also anticipating the future landscape of their roles.
Conclusion
The integration of AI into the technology sector presents both opportunities and challenges. For Product teams, leveraging AI can lead to improved processes, enhanced outputs, and ultimately, better market alignment. By understanding the potential of AI tools and adapting their skills accordingly, professionals can navigate the evolving landscape of technology businesses more effectively.
The future of technology is undoubtedly intertwined with AI, and those who are proactive in their adaptation will find themselves at the forefront of this transformation.
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