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: 2025-07-01 08:55:38
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.
Despite their potential, code-generating tools still suffer from garbage-in/garbage-out risks, similar to AI chat tools like ChatGPT. This emphasizes the importance of AI-augmented skills for human operators. The ability to effectively leverage these tools is crucial to realizing the desired value and potentially preserving jobs in the ever-evolving technological 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.
Benefits of AI for Product Teams
- Alignment: AI can help align various aspects of the product development process, ensuring that all team members are on the same page.
- Consistency: With AI-generated artifacts, teams can maintain a consistent approach to product requirements, reducing ambiguity.
- Completeness of Analysis: AI can enhance the thoroughness of analysis, leading to better-informed decisions.
While there is a general risk of homogenization of thought and approach as we become dependent on AI—similar to the effects seen with spreadsheets in Finance long ago—the benefits for Product teams can outweigh these risks. The alignment, consistency, and completeness of analysis from generated artifacts can significantly enhance the efficiency of product development.
Transforming Coders and Product Managers
Coders and Product Managers are two areas most ripe for transformation through comprehensive adoption of AI. As AI continues to evolve, the nature of coding and product management will change. It is crucial for professionals in these fields to adapt and migrate their talents to where AI is driving development.
Adapting to Change
To successfully navigate this transformation, professionals should consider the following strategies:
- Continuous Learning: Stay updated on the latest AI tools and technologies that can enhance coding and product management practices.
- Collaboration: Foster partnerships between coders and product managers to leverage AI effectively in the development process.
- Embrace Flexibility: Be open to new roles and responsibilities that may emerge as AI becomes more integrated into the workflow.
Conclusion
The landscape of technology businesses is rapidly changing, primarily due to the rise of AI. For coders and product managers, embracing this change and leveraging AI tools can lead to enhanced efficiency, better alignment, and improved outcomes. By adapting their skills and approaches, these professionals can not only survive but thrive in the evolving market.
In conclusion, the integration of AI into product teams presents both challenges and opportunities. The key to success lies in effectively navigating this landscape to ensure that technology businesses can continue to grow and innovate in the years to come.
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