How to Differentiate Your AI Product

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Welcome to Through the Noise! Today we're diving into how you can differentiate your AI product. With the flood of AI copywriting tools and noise in the market right now,?these 6 strategies I've learned will help your product stand out.

Let's dive right in.

Read time:?3 minutes

Rebel Without a Crew is a book by American filmmaker Robert Rodriguez. It chronicles how he parlayed a $7,000 16mm movie into a Hollywood career alongside his philosophy of not asking for permission and taking control of one's life.

Permissionless action can be pulled across to any creative pursuit– writing code or making content. If you want to create something and share it with the world, you don’t have to ask for anyone's approval. You can get excited about something, build something and share something with little to no friction.

The same can be said for the current state of AI. Building applications on top of large language models (LLMs) has been the latest craze in this fast-flowing water. More people making more products means a lot of commoditisation. You get a flood of people who don’t innovate when a new technology comes around. For example, there are thousands of marketing copy generators floating around by prompt engineering GPT-3. Even though the underlying models are similar, successful products will always find ways to separate themselves from the noise. Those who strive to differentiate will allow consumers to tap into a more efficient marketplace of ideas. They will take the alpha.

From immersing myself in AI over the last year, building Tribescaler to now, setting out to create the first digital second-brain powered by AI, these are the best ways I’ve found to differentiate your product.

1. Focus on solving a problem for a specific niche

Many have gone broad to have gone home. Find a problem that is burning. Red hot. One that, at first glance, only a handful of people are suffering from. How do you do that when it’s non-obvious? By speaking to lots of people. How do you speak to lots of people? By getting into an arena of interest. For me, it was writing on Twitter. I had a passion for startups and wanted to share what I was learning. So I started, slowly. I’d get 1 like on every piece I posted. The only person who’d ‘like’ my content was my girlfriend. This persisted for the first 3 months of writing online.

It was only when I realised the ‘aha’ moment that everything changed. The best content doesn’t get read, the best hooks get read. A hook is the first 1-3 lines of catchy text that grips your reader. So my co-founder Alexander and I teamed up to build a hook generator for your written content leveraging AI. Creating content → Writing → Writing on Twitter → Writing threads on Twitter → Writing the hook for your thread on Twitter. 5 layers deep. You’ve got to niche down so far that it feels uncomfortable.

2. Provide unparalleled user experience

Despite the underlying technology of AI being similar, it is the user experience that allows for superior value creation, even for those with no prior experience using AI applications. By focusing on building intuitive interfaces, you create a product that is accessible, intuitive, and enjoyable for users, driving adoption and customer delight.

Take OpenAI’s GPT-3 playground and ChatGPT. The underlying technology is very similar. But the interface is very different. The front end matters a lot. Texting and DMs are known interfaces we all use daily. Instead of chatting with a friend, OpenAI moved this interface so you could chat with the AI. Familiarity is key.

3. Separate yourself from the stack

Building infrastructure that is independent of the base model allows for easy switching between models if one were to fail. OpenAI down? I'll use AI21 Labs. This gives a plug-and-play effect, making it easier to adapt to new model developments and stay ahead of the competition. This helps to reduce the risk of model obsolescence and ensure long-term sustainability for your product.

4. Human-in-the-loop

The concept of "Human-in-the-Loop" refers to the integration of human expertise and decision-making into the AI decision-making process. This is particularly relevant in the field of reinforcement learning and RLHF (Reinforcement Learning with Human Feedback). In RLHF, the AI system interacts with the environment, learns from its experiences, and receives feedback from a human, allowing for a more efficient and effective learning process.

The idea behind RLHF is that human feedback can provide valuable information and context that is not easily captured by the AI system alone. For example, in complex and dynamic environments, such as self-driving cars or financial markets, human expertise and intuition can be used to guide the AI system towards more effective and safe decision-making. Additionally, RLHF can also help to ensure that the AI system is aligned with human values and ethical considerations, reducing the risk of unintended consequences or biasing the system.

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