By: Logan Kilpatrick

Re-posted from: https://medium.com/around-the-prompt/going-from-98-to-99-9-in-ai-is-where-all-the-work-is-ff7f1adff6e4?source=rss-2c8aac9051d3------2

How to build in the age of AI, advice from Chamath Palihapitiya

There are lots of phenomena happening in AI right now. On one hand, going from idea to code to working app has never been easier. AI has proved it can dramatically accelerate the creation of very good demos / MVPs. But where is the value created in the world? I would posit that much of it comes down to actually making things work in production. This is more true now than ever when the barrier for entry in AI continues to go down.

Tools like https://bolt.new, https://lovable.dev/, https://v0.dev and others are enabling this new wave of accelerated software creation. For the long tail of builders, these tools work very well, but one of the main limitations is how to capture the “cartilage” that makes lots of companies actually work. I had a conversation with Chamath Palihapitiya about this, and he did a great job of capturing the state of this:

So how do we get the last 2% and make some of these more difficult problems work? This is the $1,000,000 question. Right now, it still takes a lot of human work in order to translate super complex legacy processes into something powered by AI. Part of my inclination is that agents might be helpful to do this, but as Chamath mentioned, it’s likely this is going to be a “10 year process”.

One of the things I like to think about is the bitter lesson, which if folks have not heard about this can be summarized as the fact that general purpose approaches usually win out vs specialized approaches in technology specifially. In the content of getting this last 2% of reliability, you might imagine that what you go do is build a bunch of scaffolding, 100 different vertical agents, or even completely re-engineer some human system in order to work well for the age of AI. A lot of this depends on your timelines, but if you believe that model capabilities will keep scaling and generalizing to solve new problems, it is worth considering how much of an investment you should make into any one of those today, vs just waiting for the models to get good enough and solve the problem out of the box for you. The caveat here is the level of agency you should take vs waiting for the innovation to come to you is likely a factor of how much this change is going to disrupt you. If the chance is high, then you should pay the cost of building the scaffolding, doing the process re-engineering, etc in order to migrate the risk of large scale change.

At the same time as of all that is true, I was reminded by Sully this morning of just how beautiful it is that the barrier to creating software has come down 10x in the last 2 years, and what you can build has increased by 10x. The only thing that is stopping you is having an idea and the desire to solve the problem.

So yeah, solving problems in large legacy systems is not easy (regulated industries, large companies, etc), but if you just want to build 0 to 1, there has never been a better time in human history than today to do so. So go build something people want, bet on the models progressing, and make the world better along the axis you care about.

Going from 98% to 99.9% in AI is where all the work is was originally published in Around the Prompt on Medium, where people are continuing the conversation by highlighting and responding to this story.