How to Differentiate Your AI Product
Maximise Your Market Potential With These 6 Proven Strategies
Join the community of 20,777 individuals who are cutting through the noise by subscribing here today:
Hey friends 👋 ,
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
How to Differentiate Your AI Product
DALL·E prompt: "a black and white sketch of a differentiated AI"
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.
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.
Seamless integration with other apps is paramount. Everyone has their own world and their own communication channels; AI has the potential to bridge the gap between each of these systems. The ability for AI to work seamlessly across different platforms can make it more accessible for users, increasing value creation.
Integrating with popular design tools like Canva and Adobe, users can access AI-powered features within their existing workflows. This not only enhances the user experience but also provides a more comprehensive solution for businesses, making it easier for teams to collaborate and share information. The potential for AI to play a central role in bridging these systems will only continue to grow as more and more people adopt the technology. This interoperability will be critical in ensuring that AI becomes a truly integrated part of our daily lives, rather than just standalone tools.
6. Dynamic learning
By continuously adapting to users' preferences, AI systems can create a sense of familiarity and comfort. This can lead to a deeper level of engagement and loyalty from users. If wants and needs are met in a personalised way, the cost of switching is high from mental friction. It would be like leaving your best friend in search of a new companion.
The process of dynamic learning can be thought of as a "warm hug" from the AI system. It learns and evolves to meet your specific needs, creating an experience that feels more and more tailored to you as a human over time.
A Little Something Extra
🗞 Newsletter: I'm a big fan of Houck and his newsletter. He's raised over $10 million from a16z and shares tactical advice about fundraising, growth and hiring in an easily digestible way. You can join me and 14,000+ others here:
🎙 Podcast: I went deep on my story, my philosophy and everything in between in this podcast with Liam. If you want to get to know me a little better– check it out.
Let me know what you think
That’s all for today friends!
As always feel free to reply to this email or reach out @thealexbanks as I’d love to hear your feedback.
Thanks for reading and I’ll catch you next for some Sunday Signal.
If you liked this piece, subscribe below: