What happens when software gets 100X cheaper?

The end of tech debt (and other predictions)

I ask myself this question every day now.

AI’s impact on software is obvious — it will get 100X cheaper. What happens downstream of that is difficult and mind-melting to imagine. So I’m going to think through my current vantage point, out loud, unfiltered.

Tech debt will kill fewer companies

One of the interesting things we’ve found is that it’s oftentimes easier to have the AI rebuild a product from scratch than trying to do a major refactor around a new feature. Part of this is due to a current weakness in AI at understanding the context of a codebase (which will disappear) but it’s also because AI removes the idea that writing quality code takes time.

Every engineer working at a big company dreams of rewriting the codebase, or thinks angry thoughts about the new feature the CEO steamrolled into the org that created a ton of tech debt. But given how good AI is at integrating and synthesizing lots of information, this will be far less common in the future.

Tech debt will always be a thing, but the pain of YOLOing that new feature or prioritizing improving customer value over code cleanliness will go down. There will be AI systems designed to clean up after you.

New bottlenecks will emerge

The other day someone asked me whether every company will have an internal roboagency that delivers new products at lightning speed. My simple answer is that I don’t think companies would know what to do with all of those products.

If you’ve worked in a tech company, you’re familiar with the marketing or sales team complaining that the product org isn’t shipping that new thing which would really make a difference to customers. In general, engineering velocity has always been the biggest bottleneck for startups. But that’s about to change — while all functions will be augmented with AI, the transformation in engineering will happen first.

So another place to look for startup opportunities is to think about the functions around product and engineering that will become bottlenecks. For example, if the product team can launch major new features or iterations every few weeks, the ability to train sales reps or educate customers on the latest version goes from being a somewhat annoying problem to a business critical problem.

Each company will have their own OS

I think we underestimate how much software has driven cookie-cutter company structure and operations vs the other way around. Finance, legal, sales, marketing — they are indeed separate needs but the degree of standardization in how they are achieved comes in part from the siloed nature of the tools we have today.

There’s an enormous opportunity in the age of AI to build the unique operating system that represents a company and enables an emergent constellation of custom tools on top.

This is why Palantir is so well positioned to ride the AI wave in defense, because they’ve been working for a decade on mapping customer ontologies. What everyone seems to be realizing is that the hardest and most complex work to build AI enabled software for companies is data work.

It seems unlikely for there to be one big winner here. For one thing, building an OS for a specific industry isn’t just a database problem. It’s about building a language that can be used to connect the pieces, one that makes trade-offs based on the characteristics of that market. Palantir’s customers value security and secrecy above all else, and that informs how they build the platform.

The obvious place to start is with the most important data — customer data — which is one good reason Salesforce is in the crosshairs for 8090 and all of the companies pitching custom software. Land in the data and you can expand to everything else. But making the leap from a high end software agency to a platform is not easy.

More companies will have tools like FAANG

Tech giants with an excess of money and engineering talent build a huge % of their own tooling. If you’re an engineer at Google you don’t just use standard off the shelf products like a startup, you use custom versions of everything.

One specific place to find specific product opportunities would just be to audit the tools FAANG employees use, and find ones where having the custom version is a huge win for the company. Then, build a company that delivers that custom version to a lot of other companies for 10 or 100X less.

If you made it this far

Believe me I could keep going, but I’ll stop there for now. If I missed or got something wrong, I’d love to hear about it.

I’m also hosting a few workshops with leaders of forward-thinking companies over the coming weeks to explore how AI-generated custom software will drive growth or reduce costs. If you know someone great I should add to that list, please let me know.