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: 2025-06-29 21:04:04
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 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 that 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. Understanding how to properly utilize these tools will allow us to extract maximum value while potentially preserving jobs. As AI continues to evolve, the relationship between humans and machines will define the future of technology businesses.
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 build economically 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 identified needs.
- Alignment: AI tools can help ensure that product teams are aligned on their objectives and strategies.
- Consistency: By using standardized AI-generated outputs, teams can maintain consistency across their projects.
- Completeness: AI can help fill in gaps in analysis, ensuring all necessary information is considered during product development.
While there is a general risk of homogenization of thought and approach as we become dependent on AI—similar to the effects experienced with spreadsheets in finance long ago—the benefits for product teams are significant. The value derived from AI-generated artifacts can lead to better decision-making, improved efficiency, and ultimately, a stronger product offering.
Transformation of Roles
Coders and product managers are two of the areas most ripe for transformation through comprehensive adoption of AI. As technology evolves, the nature of these roles will undoubtedly change.
- Job Evolution: Coders may find themselves taking on more strategic roles that involve less manual coding and more oversight of AI-generated outputs.
- New Skills: Product managers will need to adapt by acquiring skills in AI management, data analysis, and integration strategies.
- Collaboration: Enhanced collaboration between coders and product teams will be essential as they leverage AI tools to streamline processes.
The migration of talents towards areas where AI drives them will require a proactive approach. Embracing continuous learning and remaining adaptable will be vital for professionals in the tech industry.
Conclusion
In summary, the integration of AI into product development and coding presents both challenges and opportunities. As the landscape evolves, it is crucial for entrepreneurs and professionals to remain informed and adaptable. By leveraging AI effectively, product teams can enhance their workflows, align their goals, and create products that meet the needs of an increasingly complex market.
The future of technology businesses is being shaped by AI, and those who embrace this change will be better positioned to succeed in the dynamic environment ahead.
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