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: 2025-06-26 12:40:09
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 90s, 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 Coding Tools
For anyone who has used AI coding tools like CoPilot from GitHub, it is easy to see that AI tools thrive at 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 (you and me) become critical, to get the value you want to realize and possibly to preserve jobs. As AI continues to evolve, it is vital for professionals in technology to understand how to leverage these tools effectively.
The Role of Product Managers in an AI-Driven World
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 in Product Development
- Alignment: AI tools can help streamline communication between teams, ensuring everyone is on the same page.
- Consistency: By automating certain processes, AI can help maintain uniformity in product requirements and specifications.
- Completeness: AI can analyze vast amounts of data to provide comprehensive insights that might be overlooked by human analysts.
While there is a general risk of homogenization of thought and approach as we become dependent on AI (as there was with spreadsheets in Finance long ago), the benefit for Product is alignment, consistency, and completeness of analysis from the generated artifacts produced over time.
Transforming the Roles of Coders and Product Managers
Coders and Product managers are two areas most ripe to be transformed through comprehensive adoption of AI. Jobs will change, but rather than viewing this as a threat, it is essential to understand how to migrate your talents to where AI drives them. Adaptability will be key in this evolving landscape.
Preparing for Change
- Continuous Learning: Engage in lifelong learning to stay updated on the latest AI tools and methodologies.
- Collaboration: Foster strong partnerships between Product and engineering teams to maximize the benefits of AI.
- Innovative Thinking: Encourage creative approaches to problem-solving that leverage AI capabilities.
As AI tools become more integrated into workflows, the ability to adapt and harness their potential will separate successful Product teams from the rest. This is not just about replacing traditional roles; it's about enhancing and augmenting human capabilities.
Conclusion
In conclusion, the integration of AI into product development and coding is not merely a trend but a significant shift in how technology businesses operate. By embracing this change and equipping themselves with the necessary skills, Product managers and coders can thrive in an AI-enhanced environment. The future lies in collaboration between human intelligence and artificial intelligence, leading to innovative solutions and successful products.
Word count: 735