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: 2026-04-20 20:36:09
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, and AWS to generate the templated code that is needed.
The Rise of AI in Coding
For anyone who has used AI coding tools like CoPilot from GitHub, it is easy to see that AI tools thrive in 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. Code-generating tools still suffer from garbage-in/garbage-out risks (as do AI chat tools like ChatGPT). This is where AI-augmented skills for human operators become critical, allowing us to realize value and possibly preserve jobs.
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, which 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.
While there is a general risk of homogenization of thought and approach as we become dependent on AI (similar to the impact of spreadsheets in Finance long ago), the benefits for Product teams include alignment, consistency, and completeness of analysis from the generated artifacts produced over time.
Transforming Jobs through AI
Coders and Product managers are two of the areas most ripe to be transformed through comprehensive adoption of AI. As AI continues to evolve, jobs in these fields will change significantly. It is essential to explore how to migrate your talents to where AI drives them.
Skills for the Future
- Adaptability: Embrace continuous learning and be open to new tools and methodologies.
- Collaboration: Work closely with AI systems and other team members to maximize output.
- Analytical Thinking: Develop strong analytical skills to interpret data and AI-generated insights.
- Problem-Solving: Enhance your ability to address complex issues that arise in the product development lifecycle.
Conclusion
In summary, the integration of AI into coding and product management presents both challenges and opportunities. By understanding the implications of these tools and adapting our skills accordingly, we can harness the power of AI to enhance productivity and create innovative solutions. As we look toward the future, it is crucial for entrepreneurs and professionals alike to embrace the change that AI brings, ensuring they remain relevant in the evolving landscape of technology.
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