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: 2025-07-08 01:43:01
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 (you and me) become critical, to get the value you want to realize, and possibly, to preserve the jobs.
Challenges for 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. 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.
The Transformation of Coding and Product Management
Coders and Product managers are two of the areas most ripe to be transformed through comprehensive adoption of AI. Jobs will change, and it is essential to explore how to migrate your talents to where AI drives them. The following sections delve into the specific challenges that arise in this transformation.
1. Evolving Skill Requirements
As AI tools become more prevalent, the skill sets required for both coders and product managers will shift. Professionals will need to adapt by:
- Enhancing their technical skills to work alongside AI tools.
- Learning to interpret AI-generated outputs for better decision-making.
- Focusing on strategic thinking and creativity, as these human-centric skills will remain irreplaceable.
2. The Need for Continuous Learning
With the rapid pace of technological advancements, continuous learning will be vital. Professionals should consider:
- Participating in workshops and training programs focused on AI integration.
- Staying updated with industry trends to understand how AI is reshaping roles.
- Engaging in communities of practice to share knowledge and experiences.
3. Navigating Cultural Shifts
The introduction of AI into workflows can disrupt established company cultures. To manage this transition effectively, organizations should:
- Foster an environment that embraces change and encourages innovation.
- Promote open communication about the role of AI in the workplace.
- Involve employees in the decision-making process regarding AI tool adoption.
Conclusion
The landscape of technology business is changing, driven largely by advancements in AI. Product teams must embrace these changes and adapt their strategies to stay competitive. By enhancing skills, committing to continuous learning, and navigating cultural shifts, both coders and product managers can thrive in this new AI-driven environment.
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