The AI Maximalist Software Holding Company
Alan Wells, Founder
Hi all 👋 I’m Alan, founder of Rocketable.
tl;dr
I’m building a large portfolio of wildly profitable software businesses by acquiring existing products and replacing human teams with AI agents.
https://www.youtube.com/watch?v=W80DDvHz_wo
The longer version…
I started Rocketable because I think that most of the world is still underestimating the potential of AI.
Even in Silicon Valley, most people assume that humans will maintain a monopoly on the creativity and insights needed to operate a successful software product.
I think that’s extremely unlikely.
I believe the real opportunity lies in assuming there is no job in a software company that can’t be automated by AI, and building from there.
But why are you acquiring existing products?
I believe acquiring existing products is the key to speedrunning the path to building a single person, billion dollar software company.
By acquiring existing software products that already have customers and revenue, I can focus most of my attention on the challenge of automating those businesses.
By owning the products that I automate, I can ignore the management fiefdoms, data silos, and corporate politics that prevent most companies from maximizing the upside of deploying AI in their business.
By buying increasingly larger businesses, I can force myself to climb the ladder of automation complexity faster than most individual businesses evolve organically.
So what are you building?
Although it’s obvious to me that today’s foundation models already have the raw intelligence needed to do most of the jobs in a large software company, it’s equally obvious that the tools needed to fully replace the entire org chart of a software company with AI agents do not yet exist.
In addition to being competent within each function, to operate software products with 0 human employees, the agents will also need to be:
- directed towards common goals
- integrated to collaborate across functions
- tuned with feedback loops connected to product metrics
- trained to make resource allocation decisions
Rocketable needs to build the software that enables all of those things to happen. Building the automation system while also owning the products that it operates allows me to:
- underwrite the risks of early deployment and minimize the cycle time for experimentation
- connect automation decisions to the outcomes that matter to the business (profitability, growth, retention) rather than intermediate steps in the process.
- maximize value captured from automation by keeping the upside in-house
There are also significant opportunities to improve the performance of agents (and the customer experience of products they manage) by re-thinking the approach from first principles and ignoring the assumptions that many AI-enabled vertical SaaS products are built upon.
For example: instead of automating the typical customer support workflow you see in most companies (where customers interact with relatively low skill tier 1 customer service agents when they have an issue), why not use AI to scale founder-mode support (empowering your AI support agent with access to source code, production databases, and ability to open a PR that solves a customer problem)?
What makes you think you can do this?
In 2023, I acquired a small, profitable software business on Acquire.com and started experimenting with using LLMs to automate the business. That experience gave me confidence that with the right approach to automation, there is a clear line of sight to fully automating the operations of a software business.
Prior to working on Rocketable, I spent a decade building products and leading teams that used AI/ML to automate complex tasks. I worked on self-driving cars at Cruise, self-driving trucks and ADAS systems for micromobility at Uber, and an irrigation co-pilot for farmers at Tule (YC S14). Before working on AI-enabled products, I managed traditional B2C and B2B products across web and mobile platforms.
I write code, have a degree in design, build hardware, and have also worked on optimizing other complex systems (international supply chains and field operations).
Maybe you’re not crazy. How can I help or get involved?
📨 Subscribe to updates: If you’re interested in following along as I figure out the playbook for running software businesses with teams of AI agents instead of humans, subscribe here.
🚰 Help with deal flow: I’m starting to work on my next acquisition and looking to meet owners of software products that are currently generating $250K-$600K in annual profit who may be interested in selling. If that sounds like you or someone you know, please contact me here.
🛠️ Work with Rocketable: It’s likely that I will need to hire a small team of AI-augmented humans to help build the platform that enables acquired products to operate without employees. If you have the domain mastery and technical skill needed to automate one or more of the critical functions of a software business, please contact me here.
YC Sign Photo
Company Photo
How did your company get started? (i.e., How did the founders meet? How did you come up with the idea? How did you decide to be a founder?)
I started Rocketable after acquiring a small B2C SaaS business and spending a year running it as a solo entrepreneur. I couldn’t afford to hire a team of humans to help operate the business, so I did every function in the business myself.Arguably, my co-founders are the LLMs that I interact with daily (although I haven't given them any equity - yet). It is not an exaggeration to say that I spent more time interacting with LLMs in that first year than I did with other humans.
What's the history of your company from getting started until the present day? What were the big inflection points?
Operating a small software product let me live in a future that larger companies can’t yet experience - one where I can give the LLM full context for my business, rather than just a subset of that context that is curated by human editors, company policies, or identified by semantic indexing. I found that when all source code, knowledge base articles, blog posts, internal documentation, records of recent customer support issues, and results of recent NPS surveys can fit within the context window of a model, it unlocks entirely new, AI-native solutions to the common tasks that all software businesses face. I experienced this because my business was small enough to operate within the limits of today’s largest models, and discovered that building my own custom workflows on foundation models using this full context approach led to higher performance with lower effort compared to using enterprise AI tools that have raised hundreds of millions of dollars. This was the insight that led to Rocketable being a holding company rather than a traditional product company: when it comes to operating highly automated businesses, smaller is actually better! And this means that operating a large number of smaller software businesses profitably is now possible. This is an entirely different vector for scale than what we have traditionally seen in tech startups (which usually get bigger by growing a small number of products to very large usage). The AI native future will be the opposite - a very large number of smaller products, made to serve the needs of niche users much better than today's large, monolithic products will ever be able to do, operated efficiently through automation.
What is the core problem you are solving? Why is this a big problem? What made you decide to work on it?
The problem is that humans are expensive and complicated, and it's a slippery slope - first it's a human co-founder, then it's a human first employee, and pretty soon you're a giant, bloated tech company with 100,000 humans sitting around that you need to pay and feed! There are a lot of humans working in software companies, so this is a pretty big problem (particularly if, like me, you’re obsessed with efficiency). I started working on this problem because I was disappointed by most AI products that I tried to use when operating the first business that I acquired. I wanted tools that would let me invest the effort required to reach full automation, and what I found were products that were forever stuck in co-copilot mode. After prototyping my own tools and achieving higher levels of performance with less effort, I decided to started Rocketable.
What is your long-term vision? If you truly succeed, what will be different about the world?
The long term vision for Rocketable is to turn the task of running a software business into a reinforcement learning problem. As the cost of code & content creation decreases by 1000x, product development and marketing will become probabilistic processes (with many different variations of content and features tested in parallel).When AI workflow outputs are linked to financial metrics, data driven feedback loops will compound into superhuman performance.When workflow changes are deployed via pull request, winning strategies will be deployed instantly, everywhere.This will create the conditions for the business version of the AlphaGo’s Move 37: winning business strategies that only AI can discover.