Lessons from Building an AI Company
By: Jack Pertschuk
Tags: Artificial Intelligence, Machine Learning, Product Management, Data Science, Science Fiction

Lessons from Building an AI Company

Using Science Fiction to understand the market for machine learning products

Photo by Franck V. on Unsplash

As a first time founder, I often found myself asking “what problem are we trying to solve” and seldom “what is the ultimate goal of technology and how does what we’re doing fit into it.” While you can certainly find a product-market fit by only asking the first question, I can’t really answer the personal why am I doing this without asking the second question. I also believe a company that can answer the second question has a higher ceiling to its growth — nearly all of the biggest tech companies follow this pattern.

To start let’s consider the first part of this second question, what is the ultimate goal of AI technology; then we can considering how an individual company fits into it. One way I like to visualize this “some day” eventuality is through Science Fiction. Reading or writing or watching stories about our future, and noticing what it is we imagine in a utopia, or even dystopia, can provide a detailed starting place; then we can work backwards to understand an AI company’s piece of it.

It seems silly to write, but one of the stories which first influenced my vision for AI in the future is Iron Man. Tony Stark, a superhero for engineers, became the benevolent genius inventor archetype for our generation and his virtual assistant a less dated version of Hal.

In Iron Man our protagonist builds an AI that provides everything he wants, so let’s break down each feature that he builds and correlate it with what AI products exist today, and what barriers still exist:

Indexing information at scale and providing neural QA and information retrieval with seamless support for both structured and unstructured data representations — Google

Synthesizing answers based on a set of axioms, essentially an extensible language with natural language interface — Wolfram

Interfaces to automatically model physical and digital systems and test and then synthesize or deploy them — CAD/3D printing and API Design, no-code software products, code/documentation generators

Efficiently procure move around physical objects through virtual AI interfaces. — Amazon Robotics, Alexa, AI logistics solutions.

AI logistics solutions. Everything everywhere is connected to provide real time observability and control based on AI — IoT, Wearables, iPhone, Android, tiny edge neural networks, Facebook’s drone internet infrastructure, smart cars

Technology that fixes itself — Observability/testing frameworks/Machine Learning anomaly detection

Technology that never breaks so you can rely on it with your life and is secure but distributed — In it’s simplest form CI + testing but ultimately CoqPL and proof-based “unhackable” formalized verifiable systems

Personality and fun — generative text networks e.g. Huggingface, AI Dungeon, Grammarly

Each of these AI features corresponds to a piece of the market that one or more companies are currently competing for. By understanding the entire combined problem that AI companies are trying to solve, we can identify shared barriers and corresponding opportunities for collaboration such as machine learning products that will aid the creation of other machine learning products. Only by taking this macro view, I can begin to convince myself which AI products on the market today will actually make the world a better place.

While this analysis might provide an interesting context for what an individual can get out of some of the AI technology being developed today, it lacks details of what AI can offer society. In my next post, I’ll provide further examples which allow us to explore the relationship between AI and existing social structures.