The AI Renaissance: How to stand out when everyone can build digital products

TL;DR: The rise of generative AI and custom GPTs is revolutionising digital product development, similar to how Apple's App Store's allowed software-literate people everywhere to sell apps to the world. The key to success in this new era isn't creativity or the skill to code anymore, but access to unique, proprietary data. This shift prompts questions about data ethics, the evolving role of creativity, and potential new digital divides.

ChatGPT and similar generative AI tools have already revolutionised the way we interact with our machines to create content and products. Continuous improvements of the models and releases of new widely accessible features make it easier than ever to identify business opportunities, do product discovery, and craft marketing strategies. Just recently, OpenAI - the company behind ChatGPT, released "custom GPTs" to the world.

What are custom GPTs?

GPTs are OpenAI's newest solution of creating customised versions of it's widely used ChatGPT application. These GPTs allow users to train their own versions of ChatGPT and feed it with proprietary data based on which the tool can complete specific tasks. GPTs can be integrated with various external data sources and real-world applications. Combined with the vast array of available plugins, these custom GPTs (or something similar) will likely become the foundation of a new era of digital product development. So you get an idea of the opportunities, here are some digital products which you could already build with custom GPTs:

• Automated Customer Support System: You can create a custom GPT-powered chatbot integrated with a CRM system like Salesforce via Zapier. This bot can answer customer queries, update client records, and even schedule meetings or follow-ups. The integration means all these tasks are done automatically, with minimal human oversight. No need for an expensive chatbot solution anymore.
• Personalised Email Assistant: Set up a GPT to draft and manage your emails. By connecting it to your email client and using Zapier to integrate with your calendar and to-do list apps, the AI can compose, schedule, and send emails, and even prioritise your inbox based on your schedule and priorities.
• Social Media Content Manager: Develop an AI-driven tool that curates and posts content on social media. Using GPT to generate engaging posts and connect with platforms like X, Facebook, and LinkedIn, this tool can autonomously manage your social media presence, schedule posts, and even respond to comments or queries.

As you might already assume, all these ideas were generate by my own GPT. The whole creative effort of coming up with product ideas was outsourced to my browser.

The new App Store and the democratisation of product development

It's not too far-fetched to compare this new platform to the advent of Apple's App Store. The App Store was a game-changer, much like what we're seeing with Generative AI. Think of the App Store as the starting gun for a race where small app creators could suddenly sprint alongside the tech giants. It broke down barriers, making it easier for any software-literate person with a good idea to get a digital product out there.

Now, with Generative AI, we're at a similar crossroads. It's giving individuals and small teams the power to create and compete with big players, all thanks to easy access and unique data. The big difference - coding skills are not an entry-barrier anymore. Everyone with access to GPTs can start crafting new digital products without ever having to write a line of code.

This democratisation of digital product development shifts the competition towards better marketing strategies and, most importantly, figuring out a way to stand out from the competition among (most likely) millions of competing products.

Previously, to build a successful digital product, you needed:

• an idea triggered by a problem you or someone else encountered

• an understanding of conducting product discovery and identifying the right target audience
• the means to write software and create an MVP version of your product
• a way to distribute said product to the target audience
• continuous data analytics to improve upon the MVP and sharpen the product strategy

To reach the final stages of product maturity, you needed to iterate through various steps and include teams or service providers with diverse skill sets to collect data and conduct product discovery.

With custom GPTs, it will be possible to skip the first steps all by yourself. The one resource that stays, however, is proprietary data. To get you thinking, two digital product ideas which get better with proprietary data could be:

• Targeted Marketing Platform: A marketing tool with access to proprietary consumer behaviour data can deliver more effective, personalised marketing campaigns. The more data it has towards individuals shopping behaviour, the better it can distribute whatever product its used for to distribute. A tool that knows that my iPhone's screen is broken will have a huge advantage in selling its service to someone marketing iPhone repair kits to an optimised audience.
• Mobility products: Your favourite mobility platform (e.g. e-scooters, car-sharing or ride-hailing) likely knows where you live, work, and hang out. Based on your mobility patterns and the behaviour of millions of other users, such mobility companies could develop highly specialised digital extensions to their products which no other competitor will be able to do.

Why buy a digital product when everyone can create one on their own?

Since generative AI makes creating digital products accessible to almost everyone, the question arises: why buy a digital product when you could just make one yourself? The answer lies in the unique value proposition of professionally crafted products. They often come with a level of sophistication, specialisation, and polish that DIY projects might lack. These products leverage proprietary data and expert insights to deliver a more refined, effective, and user-focused experience. While the democratisation of digital product creation is empowering, there's still a distinct advantage in products crafted by those with deep domain expertise and access to specialised data. This gap is where the true value of professionally developed digital products becomes evident.

So we just learned, central to keeping this advantage are two crucial factors: domain expertise & proprietary data.

The unique data that each digital product is trained on becomes the cornerstone of its success. The difference between a run-of-the-mill digital service and a groundbreaking one will hinge on the quality, uniqueness, and relevance of the training data. It’s not just about having data; it's about having the right data.

Here are some thought-provoking questions to consider in this new era:

1. Will the emphasis on unique data lead to a new form of digital divide, privileging those with access to more exclusive or comprehensive datasets?
2. How will the role of creativity evolve when the key differentiator is the data feeding the AI?
3. In what ways might this reliance on proprietary data impact the ethics and privacy concerns in AI development?

As we go through this GenAI Renaissance, these questions prompt us to rethink the essence of digital product development. It’s a journey from creativity as we know it to a time in which data shapes the future of digital services more than ever.

Conclusion

The key differentiator in this new era is no longer just creativity or technical expertise, but the proprietary data used to train AIs. This shift could lead to a new digital divide based on data access, raising questions about ethics, privacy, and the role of creativity in AI development. What are your thoughts on this shift in digital product development? Have you tried creating your own GPTs already? If so, what does it do? Let me know in the comments!