What AI means for building new products

I’ve been very excited about AI for years, and have worked with Deep Learning, NLP, formal logic, and Bayesian Statistics methods for over a decade. However, one problem that we’ve had is “making AI useful”. We haven’t really had a new paradigm as such in computing for a while. I feel this is the top story in tech in 2022

Now it seems that it is Generative AI, foundation models and Large Language models.

A good talk is by Greg Brockman on Open AI which comments on some of the problems/ opportunities

We’ve seen a range of tools in a range of verticals, and a lot of VC commentary about Generative AI and of course the hype about ChatGPT so while for the last 10 years AI may have seemed like a ‘nice to have’ now it is very much affecting product evolution. 

One of the first things we saw in this space was in the Image generation space. 

Let’s give some definitions: 

Generative AI is an umbrella term for a number of machine learning methods — including Large Language Models (LLMs) and Generative Adversarial Networks (GANs). What has been most exiting in recent months have been the improvements in “foundational models” such as GPT3, PaLM, and various others – these are enabling media generation – at almost a human level. We’re also seeing some impressive innovation from companies such as Stability.AI and RunwayML

Source: https://www.craiyon.com/ 

At Aflorithmic we’re leveraging some of these technologies to make generating audio easier. Fundamentally what makes that exciting is that you’re leveraging technology that didn’t exist before to do “new things”. We had similar changes in mobile 10-15 years ago, and now it seems that there’s a coalescing of understanding, technological changes and consumer changes to unveil new products. We’ve also had improvements in moores law, an explosion of data on the internet (wikipedia for example) and improvements in training models. Not to mention a lot of innovation in the open source community. 

I’ve been working at this intersection for a while. And I simply want to bring some hard earned advice for working on AI-enabled products. I only want to encourage other entrepreneurs, to take advantage of this coming revolution. 

I think firstly like anything this is a new area to explore, and I simply want to remind you all there is no map. I’ve been working with these technologies on products for developers and consumers for nearly a decade. And I’m amazed at some of the innovation that’s happening. 

I’ve tried to distill some learning

So here are 5 principles for building good AI-native products and AI-native companies

• Distribution matters There can be all sorts of wedges like widgets, plugins or chrome extensions that can get you the distribution you need. You may need to reuse the same underlying technology in a variety of ways. Startups always need to find distribution
• User experience and a feedback loop matters. I feel we’re also still figuring out what’s acceptable in AI-native products – one of the good insights that Github copilot had was that it wasn’t ‘annoying’ as AI will get some things wrong (what is sometimes called “hallucination”), we need to be careful to not overwhelm users or “surprise” them. 
• Use R and D to your advantage. While it can be good to use existing APIs and existing models, you often need to tailor R and D to suit your workflows. So that means R and D/ML is crucial and you need to be building up those skills in your team, to have a long term competitive advantage
• Unlock new workflows. What does this technology allow you to do that previous paradigms didn’t? While it can be good to replicate existing workflows, it can be a good starting point. Great products allow you to do things that you couldn’t do before. One example in AI-products we’re already seeing is “generating 100 variants of the same media with slightly different parameters” that’s something that was hugely cost prohibitive before. 
• Latency matters – intelligent use of caching, the right models and the various tradeoffs of model size are very important for managing the cost of a product but also more importantly the customer experience. Some of these learnings only happen when you ship something to users, so ship iteratively!

So go forth and build – I can’t wait to hear what you’re working on!

Some further reading

• https://arxiv.org/pdf/2207.10342.pdf (A great paper on cascades – what’s the user interface for this) 
• A great blog by Nat Friedman is www.aigrant.org
• Intercom has a great blog on this too

If you have any feedback I’m peadarcoyle[at]gmail[dot]com or peadar[at]aflorithmic[dot]ai or you can find me screwing about on twitter – under springcoil